[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$ffFUljuElgdP-i1mZ4wzexYr4TTvUKDDrmCNWTaL4Fwo":3,"$f-gwIXJgDGgGv9bmRu29vUWmMAqDVQVCWspFuwnTv5fs":28,"$fs8386zdAUI-Rj5TzVbzTerjIHduHTXuIcIJmyYrxqs0":127,"$fBuPzgYSZ2qvCIvMieT5y4oi-25q1ef7qJGzWFioAT14":300,"$fInp5A0Yn6lpuNgrVqLP8Yeofi1GnSABzRYwHU9L8AVs":797,"notifications-unread":1095,"$fXAavh3Oi1XbGAMk_uIhM9fVa-50p2EYs84kFeUjyEZY":1643},[4,9,14,19,24],{"id":5,"slug":6,"name":7,"color_token":8},"5878a593-ac48-4ff3-a25b-35a7de448daa","narzedzia-ai","Narzędzia AI","accent",{"id":10,"slug":11,"name":12,"color_token":13},"ec013b2c-e6c4-4c31-b1ec-9587c629a169","praktyczne-zastosowania","Praktyczne zastosowania","gold",{"id":15,"slug":16,"name":17,"color_token":18},"165808b7-bea9-4eef-8280-014b3d85e6bf","news-analizy","News & analizy","azure",{"id":20,"slug":21,"name":22,"color_token":23},"d6c684e6-7b4c-40d9-907b-469f30dc2126","tutoriale-how-to","Tutoriale how-to","olive",{"id":25,"slug":26,"name":27,"color_token":8},"73506ae5-4a59-4ce3-9712-58fbb8565fc4","nauka-biznes-marketing","Nauka biznes & marketing",[29,34,39,44,49,53,58,63,68,72,77,82,87,92,97,102,107,112,117,122],{"slug":30,"draft_id":31,"pillar_id":33},"google-vs-anthropic-kto-naprawde-kontroluje-open-source-ai",{"title":32},"Google vs Anthropic: kto naprawdę kontroluje open source AI?",{"name":17},{"slug":35,"draft_id":36,"pillar_id":38},"78-deklaruje-5-mierzy-gdzie-reszta-gubi-pieniadze",{"title":37},"78% deklaruje, 5% mierzy. Gdzie reszta gubi pieniądze?",{"name":27},{"slug":40,"draft_id":41,"pillar_id":43},"27-razy-zbudowalem-tego-samego-bota-oto-co-naprawde-psuje-sie-po-wyjsciu-z-demo",{"title":42},"Zapier, Make czy n8n: agenci AI i automatyzacja bez kodu w 2026",{"name":7},{"slug":45,"draft_id":46,"pillar_id":48},"news-analizy-nawigacja-w-dynamicznym-swiecie-ai",{"title":47},"Newsy i analizy AI w 2026: jak odróżnić zmianę realną od szumu",null,{"slug":50,"draft_id":51,"pillar_id":48},"praktyczne-zastosowania-ai-od-pilotazu-do-produkcji",{"title":52},"Praktyczne zastosowania AI: jak przejść od pilotażu do produkcji",{"slug":54,"draft_id":55,"pillar_id":57},"90-inwestuje-12-mierzy-to-jest-roznica-miedzy-zyskiem-a-wydatkiem",{"title":56},"90% inwestuje, 12% mierzy. To jest różnica między zyskiem a wydatkiem.",{"name":27},{"slug":59,"draft_id":60,"pillar_id":62},"web-search-rag-pipeline-zanim-napiszesz-kolejny-scraper-przeczytaj-o-czterech-pulapkach",{"title":61},"Twój scraper spala tokeny na menu nawigacyjnym. Oto co zrobić zamiast tego",{"name":12},{"slug":64,"draft_id":65,"pillar_id":67},"hermes-agent-vs-gpt-4-kto-naprawde-wygrywa-w-open-source",{"title":66},"Hermes Agent vs. GPT-4: Kto naprawdę wygrywa w open-source?",{"name":17},{"slug":69,"draft_id":70,"pillar_id":48},"tutoriale-how-to-praktyczne-integracje-ai-krok-po-kroku",{"title":71},"Tutoriale how-to: Praktyczne integracje AI krok po kroku",{"slug":73,"draft_id":74,"pillar_id":76},"twoj-pilot-ai-zachwycil-zarzad-dlaczego-nigdy-nie-trafi-na-hale-produkcyjna",{"title":75},"95% pilotaży AI nie trafia do produkcji — i nie chodzi o kod",{"name":12},{"slug":78,"draft_id":79,"pillar_id":81},"twoj-agent-ma-72-skutecznosci-zmiana-modelu-tego-nie-naprawi",{"title":80},"LLM jako selektor akcji: dlaczego 72% to pułap, którego nie przeskoczysz bez zmiany architektury",{"name":12},{"slug":83,"draft_id":84,"pillar_id":86},"openai-i-microsoft-kto-zyskuje-kto-traci-na-zerwaniu-wylacznosci",{"title":85},"OpenAI i Microsoft: kto zyskuje, kto traci na zerwaniu wyłączności",{"name":17},{"slug":88,"draft_id":89,"pillar_id":91},"5-dni-zero-kodu-dzialajacy-agent-ai-czy-google-naprawde-da-founderom-to-czego-potrzebuja",{"title":90},"5 dni, zero kodu, działający agent AI – czy Google naprawdę da founderom to, czego potrzebują?",{"name":22},{"slug":93,"draft_id":94,"pillar_id":96},"karpathy-w-anthropic-kto-teraz-naprawde-wyprzedzi-openai",{"title":95},"Karpathy w Anthropic: kto teraz naprawdę wyprzedzi OpenAI?",{"name":17},{"slug":98,"draft_id":99,"pillar_id":101},"chatgpt-w-rozmowach-o-depresji-czy-zwolnieniach-kiedy-ai-naprawde-rozumie-o-co-chodzi",{"title":100},"ChatGPT w rozmowach o depresji czy zwolnieniach — kiedy AI naprawdę rozumie, o co chodzi?",{"name":17},{"slug":103,"draft_id":104,"pillar_id":106},"twoj-agent-ai-wlasnie-zjadl-trucizne-i-nawet-o-tym-nie-wie",{"title":105},"Twój agent AI właśnie przeczytał stronę, która go okłamała — i nie wie o tym",{"name":12},{"slug":108,"draft_id":109,"pillar_id":111},"kiedy-brak-llm-gateway-zaczyna-kosztowac-mapa-decyzji-dla-operatora",{"title":110},"Kiedy jeden provider LLM zaczyna kosztować więcej niż cały zespół",{"name":12},{"slug":113,"draft_id":114,"pillar_id":116},"egzaminy-w-erze-ai-czy-chatgpt-zdalby-mature-lepiej-niz-twoje-dziecko",{"title":115},"Egzaminy w erze AI: Czy ChatGPT zdałby maturę lepiej niż twoje dziecko?",{"name":17},{"slug":118,"draft_id":119,"pillar_id":121},"nemotron-3-nano-vs-reszta-stawki-4b-kto-wygrywa-lokalny-benchmark-finansowy-w-2026",{"title":120},"Nemotron 3 Nano 4B: co potwierdzają źródła o modelu NVIDIA",{"name":7},{"slug":123,"draft_id":124,"pillar_id":126},"15-minut-vs-30-sekund-jak-przyspieszyc-pandasa-bez-kupowania-nowego-serwera",{"title":125},"15 minut vs 30 sekund: jak przyspieszyć Pandasa bez kupowania nowego serwera",{"name":22},[128,160,186,212,237,258,278],{"id":129,"label":130,"label_en":131,"icon":132,"subcategories":133,"count":159},"generowanie-tresci","Generowanie treści","Content Generation","pen-tool",[134,139,144,149,154],{"id":135,"label":136,"description":137,"count":138},"tekst-llm","Tekst i LLM","Chatboty, asystenci pisania, generatory copy, parafrazy",28,{"id":140,"label":141,"description":142,"count":143},"obrazy-design","Obrazy i design","Generowanie obrazów, edycja zdjęć, design graficzny",115,{"id":145,"label":146,"description":147,"count":148},"wideo-animacja","Wideo i animacja","Generowanie wideo, edycja, animacja AI",58,{"id":150,"label":151,"description":152,"count":153},"audio-mowa","Audio i mowa","TTS, voice cloning, generowanie muzyki, podcasty AI",43,{"id":155,"label":156,"description":157,"count":158},"kodowanie","Kodowanie","Asystenci kodowania, generacja kodu, code review",18,262,{"id":161,"label":162,"label_en":163,"icon":164,"subcategories":165,"count":185},"automatyzacja","Automatyzacja","Automation","zap",[166,170,175,180],{"id":167,"label":168,"description":169,"count":153},"workflow","Workflow i integracje","Automatyzacja procesów, integracje API, no-code",{"id":171,"label":172,"description":173,"count":174},"agenci-ai","Agenci AI","Autonomiczni agenci, frameworki agentowe, MCP",51,{"id":176,"label":177,"description":178,"count":179},"web-scraping","Web scraping i harvesting","Ekstrakcja danych, scraping, monitoring stron",6,{"id":181,"label":182,"description":183,"count":184},"rpa","RPA i automatyzacja biura","Robotic Process Automation, automatyzacja zadań biurowych",0,100,{"id":187,"label":188,"label_en":189,"icon":190,"subcategories":191,"count":211},"biznes-marketing","Biznes i marketing","Business & Marketing","briefcase",[192,197,202,207],{"id":193,"label":194,"description":195,"count":196},"marketing-seo","Marketing i SEO","SEO, content marketing, reklamy, social media",76,{"id":198,"label":199,"description":200,"count":201},"sprzedaz-crm","Sprzedaż i CRM","CRM AI, lead generation, sales automation",39,{"id":203,"label":204,"description":205,"count":206},"analityka","Analityka i BI","Business Intelligence, analiza danych, raportowanie",7,{"id":208,"label":209,"description":210,"count":179},"finanse-ksiegowosc","Finanse i księgowość","Automatyzacja finansów, fakturowanie, księgowość AI",128,{"id":213,"label":214,"label_en":215,"icon":216,"subcategories":217,"count":201},"produktywnosc","Produktywność","Productivity","check-square",[218,222,227,232],{"id":219,"label":220,"description":221,"count":206},"asystenci","Asystenci AI","Asystenci osobisti, AI chat, asystenci głosowi",{"id":223,"label":224,"description":225,"count":226},"notatniki-pm","Notatniki i zarządzanie projektami","AI note-taking, project management, organizacja 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randki, gry, kreatywność",10,{"id":295,"label":296,"description":297,"count":298},"inne","Inne","Nieskategoryzowane narzędzia AI",12,35,{"data":301,"_meta":794,"alternatives":795,"deals":796},{"source":302,"source_key":303,"source_page_url":304,"name":305,"slug":306,"description":307,"title":308,"category":309,"access_model":310,"pricing_evidence":311,"capability_evidence":315,"headings":326,"breadcrumbs":331,"pros":332,"cons":344,"external_links":349,"affiliate_links":411,"official_link_candidates":412,"text_excerpt":423,"fetched_at":424,"final_url":304,"http_status":425,"content_type":426,"content_hash":427,"cache_file":428,"published":429,"verification_status":430,"official_url":431,"official_evidence":432,"api_available":675,"blog_titles":676,"data_region":680,"data_training":681,"entity_type":682,"external_rating":680,"features":683,"free_summary":702,"gdpr":680,"integrations":703,"languages":719,"license":722,"limitations":723,"model_names":732,"polish_support":734,"pricing_plans":735,"pricing_summary":743,"privacy_summary":744,"release_date":745,"research_links":746,"research_summary":747,"security_summary":748,"self_hosted":675,"short_description":749,"starting_price":739,"supported_platforms":750,"target_users":757,"trial_summary":680,"use_cases":765,"vendor":781,"status":782,"last_verified_at":783,"alternatives":784,"quality":789,"category_v3":265,"category_v3_reason":791,"deep_card":792,"deep_card_generated":429,"category_v3_main":259,"category_v3_label":266,"category_v3_main_label":260,"_category_fixed":429,"evidence":793},"AIxploria","aixploria","https:\u002F\u002Fwww.aixploria.com\u002Finkling-thinking-machines\u002F","Inkling","inkling-thinking-machines","Inkling (par Thinking Machines) est un modèle IA open-weights de 975B paramètres, multimodal, avec raisonnement contrôlable et fine-tuning disponible sur Tinker.","Inkling : Avis, Prix, Info & 41 IA Alternatives | 2026 | AIxploria","audio","open_source",[312,313,314],"97 Visiter Freemium Kimi K3","248 Visiter Freemium GPT‑5.5 Instant","89 Visiter Freemium Claude Sonnet 5",[316,317,318,319,320,321,322,323,324,325],"Inkling est le premier modèle d'IA en poids ouverts de Thinking Machines Lab, le laboratoire fondé par Mira Murati, ancienne directrice technique d'OpenAI. Ses poids se téléchargent gratuitement sous licence Apache 2.0; seule l'inférence, sur vos machines ou via une API partenaire, se paie. Sous le capot, un mélange d'experts de 975 milliards de paramètres , dont 41 milliards actifs par requête, et une fenêtre de contexte d'un million de tokens. Il lit texte, images et audio, puis répond en texte, code compris.","Avantages Poids téléchargeables et modifiables sous licence Apache 2.0 Comprend nativement texte, images et audio Fenêtre de contexte d'un million de tokens Effort de réflexion réglable pour maîtriser les coûts Déclinaison Inkling-Small pour les configurations modestes Inconvénients Sorties limitées au texte, sans génération d'images Version complète réservée aux gros clusters GPU Performance brute en retrait des tout meilleurs modèles Trois sens en entrée, un curseur d'effort en sortie","Voie d'accès Ce que vous obtenez Pour qui Inkling Playground essai conversationnel dans le navigateur les curieux, sans installation Hugging Face poids complets BF16, NVFP4 et variantes GGUF équipes disposant d'un cluster GPU Tinker fine-tuning sur vos données, contexte 64K ou 256K personnalisation métier API partenaires (Together AI, Fireworks, Baseten, Modal, Databricks) inférence hébergée facturée à l'usage intégration dans vos applications Essayer Inkling gratuitement Questions fréquentes","Inkling est-il gratuit ? Oui, les poids d'Inkling se téléchargent gratuitement sur Hugging Face sous licence Apache 2.0. Le coût se déplace vers l'exécution: soit votre propre matériel, soit une API facturée à l'usage chez Together AI, Fireworks, Baseten, Modal ou Databricks. Le fine-tuning via Tinker se paie également.","Inkling est-il vraiment open source ? Techniquement, Inkling est un modèle à poids ouverts plutôt qu'open source complet: la licence Apache 2.0 couvre usage commercial, modification et redistribution sans redevance, mais les données et le code d'entraînement restent privés. C'est le même standard que les publications de Llama ou DeepSeek.","Verdict : Champion des classements, non; matière première, oui: si votre équipe cherche un modèle multimodal à affiner sur ses propres données sans dépendre d'une API fermée, Inkling est l'une des fondations ouvertes les plus ambitieuses à l'ouest du Pacifique.","« Créez rapidement des miniatures accrocheuses pour vos vidéos sur YouTube, Twitch et Facebook. Attirez plus de vues et améliorez facilement le taux de clics sur vos clip vidéos »","« Le modèle LLM de chez Moonshot qui rivalise avec les meilleurs modèles propriétaires sur de nombreux benchmarks. Conçu pour le codage longue durée, le raisonnement avancé et les tâches de knowledge work, c'est aussi le premier modèle open source à franchir la barre des 2,8 trillions de paramètres»","« Le dernier modèle d’OpenAI pour ceux qui ont besoin d’un IA plus forte en code, en science et en cybersécurité. Disponible directement dans ChatGPT ou via l'API en trois versions : Sol, Terra et Luna»","« Exploitez le modèle IA d'Anthropic, plus fiable, plus honnête et 4 fois moins enclin à laisser passer ses propres erreurs. Son mode rapide délivre jusqu'à 2,5x plus de tokens par seconde (au même tarif API que la version précédente)",[327,328,329,330],"Trois sens en entrée, un curseur d'effort en sortie","Inkling, une base à façonner plutôt qu'un trophée de podium","Où essayer et télécharger Inkling","Questions fréquentes",[],[333,334,335,336,337,338,339,340,341,342,343],"Natywnie obsługuje tekst, obraz i audio.","Bardzo długi kontekst 1M tokenów.","Open weights i licencja Apache 2.0 sprzyjają użyciu komercyjnemu i fine-tuningowi.","Dobre dopasowanie do agentic coding, tool use, RAG i modeli konwersacyjnych.","Dostępne ścieżki wdrożenia: self-hosting, Tinker i partnerzy API.","Open weights under permissive Apache 2.0 license","Multimodal input (text, images, audio)","Extremely large context window (1M tokens)","Adjustable reasoning effort for cost control","Fine-tuning available via Tinker","Supports tool calling and structured output",[345,346,347,348],"Bardzo duży model, więc self-hosting wymaga znacznych zasobów GPU i infrastruktury.","Model nie jest najsilniejszy ogólnie spośród modeli open i closed, według własnego opisu producenta.","Jak każdy duży model bazowy może halucynować, gorzej trzymać się instrukcji w długich rozmowach i wykazywać bias zależny od danych treningowych.","Producer notes residual risk with role-play and indirectly framed harmful prompts; downstream defenses are recommended.",[350,353,355,357,359,362,365,368,371,374,377,380,382,384,387,389,390,393,394,395,398,400,401,403,405,407,410],{"url":351,"text":352},"https:\u002F\u002Ftwitter.com\u002Fintent\u002Ftweet?url=https%3A%2F%2Fwww.aixploria.com%2Finkling-thinking-machines%2F&text=Inkling%3A+D%C3%A9couvrez+le+premier+LLM+de+chez+Thinking+Machines+%3A+un+mod%C3%A8le+ouvert+et+multimodal+avec+un+contexte+allant+jusqu%27%C3%A0+1+million+de+tokens.+Il+a+%C3%A9t%C3%A9+con%C3%A7u+principalement+pour+le+code%2C+la+manipulation+d%27outils+et+le+raisonnement+adaptatif+%28avec+des+possibilit%C3%A9s+de+fine-tuning+via+Tinker%29.","",{"url":354,"text":352},"https:\u002F\u002Fwww.facebook.com\u002Fsharer\u002Fsharer.php?u=https%3A%2F%2Fwww.aixploria.com%2Finkling-thinking-machines%2F",{"url":356,"text":352},"https:\u002F\u002Fwww.linkedin.com\u002FshareArticle?url=https%3A%2F%2Fwww.aixploria.com%2Finkling-thinking-machines%2F&title=Inkling",{"url":358,"text":352},"https:\u002F\u002Ft.me\u002Fshare\u002Furl?url=https%3A%2F%2Fwww.aixploria.com%2Finkling-thinking-machines%2F&text=D%C3%A9couvrez+le+premier+LLM+de+chez+Thinking+Machines+%3A+un+mod%C3%A8le+ouvert+et+multimodal+avec+un+contexte+allant+jusqu%27%C3%A0+1+million+de+tokens.+Il+a+%C3%A9t%C3%A9+con%C3%A7u+principalement+pour+le+code%2C+la+manipulation+d%27outils+et+le+raisonnement+adaptatif+%28avec+des+possibilit%C3%A9s+de+fine-tuning+via+Tinker%29.",{"url":360,"text":361},"https:\u002F\u002Fchatgpt.com\u002F?q=Donne-moi%20une%20pr%C3%A9sentation%20claire%20et%20concise%20de%20Inkling.%20Explique%20%C3%A0%20quoi%20sert%20cet%20outil%20IA%2C%20%C3%A0%20qui%20il%20s%E2%80%99adresse%2C%20ses%20principales%20fonctionnalit%C3%A9s%2C%20son%20mod%C3%A8le%20tarifaire%2C%20ses%20points%20forts%2C%20ses%20limites%20et%20ses%20alternatives%20possibles.","ChatGPT",{"url":363,"text":364},"https:\u002F\u002Fwww.perplexity.ai\u002F?q=Recherche%20des%20informations%20%C3%A0%20jour%20sur%20Inkling.%20R%C3%A9sume%20%C3%A0%20quoi%20sert%20cet%20outil%2C%20ses%20prix%20actuels%20si%20disponibles%2C%20ses%20principales%20fonctionnalit%C3%A9s%2C%20ses%20points%20forts%2C%20ses%20limites%20et%20ses%20alternatives%20notables.","Perplexity",{"url":366,"text":367},"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002Finkling","Hugging Face",{"url":369,"text":370},"https:\u002F\u002Ftinker.thinkingmachines.ai\u002Fplayground","Playground",{"url":372,"text":373},"https:\u002F\u002Fthinkingmachines.ai\u002Fnews\u002Fintroducing-inkling\u002F","Info",{"url":375,"text":376},"https:\u002F\u002Ffr.wikipedia.org\u002Fwiki\u002FGrand_mod%C3%A8le_de_langage","modèle LLM",{"url":378,"text":379},"https:\u002F\u002Fget.emergent.sh\u002Fe8utricukk6y","Emergent AI",{"url":378,"text":381},"« Transformez vos idées en applications complètes grâce à des agents IA qui codent, testent et déploient pour vous. Créez sites web (full-stack), apps IA, jeux et outils automatisé",{"url":378,"text":383},"Visiter",{"url":385,"text":386},"https:\u002F\u002Ftry.web.clickup.com\u002Fgslcwsp7r8e2","ClickUp",{"url":385,"text":388},"« Gérez vos projets plus vite avec une plateforme qui regroupe équipes, tâches et documents en un seul espace. Son IA intégrée résume vos notes, rédige pour vous et organise automa",{"url":385,"text":383},{"url":391,"text":392},"https:\u002F\u002Fwww.thumbnailcreator.com\u002F","ThumbnailCreator.com",{"url":391,"text":322},{"url":391,"text":383},{"url":396,"text":397},"https:\u002F\u002Fvmake.ai\u002Fvideo-enhancer","Vmake Video Enhancer",{"url":396,"text":399},"« Améliorez vos vidéos floues et de mauvaise qualité pour les transformer en HD et 4K grâce à l'IA. Vmake accentue les détails de vos vidéos, améliore leur netteté et leur donne un",{"url":396,"text":383},{"url":402,"text":352},"https:\u002F\u002Ftwitter.com\u002FAixploria",{"url":404,"text":352},"https:\u002F\u002Fwww.youtube.com\u002F@aixploria-off?sub_confirmation=1",{"url":406,"text":352},"https:\u002F\u002Fwww.tiktok.com\u002F@aixploria",{"url":408,"text":409},"https:\u002F\u002Fcookiedatabase.org\u002Ftcf\u002Fpurposes\u002F","En savoir plus sur ces finalités",{"url":408,"text":409},[],[413,414,415,416,417,418,419,420,421,422],{"url":358,"text":352},{"url":360,"text":361},{"url":363,"text":364},{"url":366,"text":367},{"url":369,"text":370},{"url":372,"text":373},{"url":375,"text":376},{"url":378,"text":379},{"url":378,"text":381},{"url":378,"text":383},"Inkling : Avis, Prix, Info & 41 IA Alternatives | 2026 | AIxploria \nTop 10 Catégories IA Tutoriels IA + Plus Nouveaux Outils IA Outils IA Gratuits Actualités IA 2026 Bonus & Extras Soumettre une IA \nEnglish Français Español Deutsch Italiano Português + More × 中文 日本語 العربية हिन्दी Русский 한국어 Türkçe Tiếng Việt فارسی বাংলা Indonesia ไทย Nederlands Polski اردو Kiswahili Українська Ελληνικά עברית Svenska Norsk Dansk Suomi Čeština Magyar Melayu Tagalog 中文 (香港) தமிழ் \nS'inscrire \nTop 10 Catégories IA Tutoriels IA + Plus Nouveaux Outils IA Outils IA Gratuits Actualités IA 2026 Bonus & Extras Soumettre une IA \nS'inscrire \nAccueil IA Récentes Inkling \nInkling \n#25 dans Modèles LLM \n4.4\u002F5 \nVisiter ce site \n« Découvrez le premier LLM de chez Thinking Machines : un modèle ouvert et multimodal avec un contexte allant jusqu'à 1 million de tokens. Il a été conçu principalement pour le code, la manipulation d'outils et le raisonnement adaptatif (avec des possibilités de fine-tuning via Tinker).\n»\nEn savoir plus : \nChatGPT \nPerplexity \nGratuit \n59924 \nLiens utiles \nSite Officiel \nHugging Face \nPlayground \nInfo \n#IA Récentes \n#Modèles LLM C'est un nouvel outil IA \n69 Inkling : 975 milliards de paramètres à poids ouverts signés Mira Murati\nInkling est le premier modèle d'IA en poids ouverts de Thinking Machines Lab, le laboratoire fondé par Mira Murati, ancienne directrice technique d'OpenAI. Ses poids se téléchargent gratuitement sous licence Apache 2.0; seule l'inférence, sur vos machines ou via une API partenaire, se paie. Sous le capot, un mélange d'experts de 975 milliards de paramètres , dont 41 milliards actifs par requête, et une fenêtre de contexte d'un million de tokens. Il lit texte, images et audio, puis répond en texte, code compris.\nAvantages Poids téléchargeables et modifiables sous licence Apache 2.0 Comprend nativement texte, images et audio Fenêtre de contexte d'un million de tokens Effort de réflexion réglable pour maîtriser les coûts Déclinaison Inkling-Small pour les configurations modestes Inconvénients Sorties limitées au texte, sans génération d'images Version complète réservée aux gros clusters GPU Performance brute en retrait des tout meilleurs modèles Trois sens en entrée, un curseur d'effort en sortie\nInkling accepte du texte, des images et de l'audio, et produit du texte, y compris du code et des données structurées. Ce modèle LLM repose sur une architecture en mélange d'experts: sur ses 975 milliards de paramètres, seuls 41 milliards se réveillent à chaque requête, ce qui allège la facture d'inférence. L'entraînement a englouti 45 000 milliards de tokens mêlant texte, images, audio et vidéo.\nSon réglage le plus malin au quotidien: l'effort de réflexion. Vous baissez le curseur, la réponse tombe plus vite et consomme moins; vous le montez, le modèle creuse. Sur un test de code, Thinking Machines annonce le même score que le Nemotron 3 Ultra de NVIDIA avec trois fois moins de tokens. Et plutôt que d'inventer, Inkling a été entraîné à signaler ce dont il n'est pas sûr.\nBenchmarks, architecture et tests réels d'Inkling Première prise en main d'Inkling, du code au multimodal Inkling, une base à façonner plutôt qu'un trophée de podium\nThinking Machines assume qu'Inkling n'est pas le modèle le plus fort du marché, poids ouverts ou fermés confondus. Le pari est ailleurs: livrer un socle équilibré que chaque organisation affine ensuite sur ses propres données via Tinker, le service maison de fine-tuning, avec des contextes de 64K ou 256K tokens. À sa sortie, il était le plus gros modèle américain à poids ouverts, en réplique directe aux DeepSeek V4, GLM 5.2 et Kimi K2.6 qui trustent le haut du classement des modèles LLM .\nQuelques signes particuliers relevés au lancement:\n77,6 % sur SWE-bench Verified, devant Nemotron 3 en ingénierie logicielle parmi les modèles ouverts les plus solides en compréhension audio (VoiceBench, MMAU) capable d'écrire ses propres scripts de fine-tuning via un agent de code entraîné à répondre franchement sur les sujets exposés à la censure un petit frère, Inkling-Small, 276 milliards de paramètres dont 12 actifs Où essayer et télécharger Inkling\nLe chemin le plus court passe par l'Inkling Playground, un chat mis en ligne gratuitement au lancement. Pour un usage sérieux, plusieurs voies coexistent (votre PC de bureau, lui, déclinera poliment l'invitation pour la version complète).\nVoie d'accès Ce que vous obtenez Pour qui Inkling Playground essai conversationnel dans le navigateur les curieux, sans installation Hugging Face poids complets BF16, NVFP4 et variantes GGUF équipes disposant d'un cluster GPU Tinker fine-tuning sur vos données, contexte 64K ou 256K personnalisation métier API partenaires (Together AI, Fireworks, Baseten, Modal, Databricks) inférence hébergée facturée à l'usage intégration dans vos applications Essayer Inkling gratuitement Questions fréquentes\nInkling est-il gratuit ? Oui, les poids d'Inkling se téléchargent gratuitement sur Hugging Face sous licence Apache 2.0. Le coût se déplace vers l'exécution: soit votre propre matériel, soit une API facturée à l'usage chez Together AI, Fireworks, Baseten, Modal ou Databricks. Le fine-tuning via Tinker se paie également.\nInkling est-il vraiment open source ? Techniquement, Inkling est un modèle à poids ouverts plutôt qu'open source complet: la licence Apache 2.0 couvre usage commercial, modification et redistribution sans redevance, mais les données et le code d'entraînement restent privés. C'est le même standard que les publications de Llama ou DeepSeek.\nQuelle machine faut-il pour faire tourner Inkling en local ? Du très lourd: le point de contrôle BF16 réclame 2 To de VRAM cumulée, la version quantisée NVFP4 se contente d'environ 600 Go, et des GGUF compressés circulent pour llama.cpp. Sur une machine modeste, la piste réaliste s'appelle Inkling-Small et ses 12 milliards de paramètres actifs.\nInkling ou DeepSeek, lequel choisir ? Les deux jouent dans la même cour des grands modèles ouverts, DeepSeek V4 côté chinois, Inkling côté américain. Les premiers comparatifs donnent l'avantage à certains rivaux sur le code en terminal, mais Inkling reste le seul du lot à écouter de l'audio et lire des images nativement, avec un contexte d'un million de tokens.\nVerdict : Champion des classements, non; matière première, oui: si votre équipe cherche un modèle multimodal à affiner sur ses propres données sans dépendre d'une API fermée, Inkling est l'une des fondations ouvertes les plus ambitieuses à l'ouest du Pacifique.\n★ Outils IA de Prestige ★ \n★ Mis en avant \nEmergent AI \n« Transformez vos idées en applications complètes grâce à des agents IA qui codent, testent et déploient pour vous. Créez sites web (full-stack), apps IA, jeux et outils automatisés simplement en décrivant votre projet »\nVisiter \n★ Mis en avant \nClickUp \n« Gérez vos projets plus vite avec une plateforme qui regroupe équipes, tâches et documents en un seul espace. Son IA intégrée résume vos notes, rédige pour vous et organise automatiquement vos priorités »\nVisiter \n★ Mis en avant \nThumbnailCreator.com \n« Créez rapidement des miniatures accrocheuses pour vos vidéos sur YouTube, Twitch et Facebook. Attirez plus de vues et améliorez facilement le taux de clics sur vos clip vidéos »\nVisiter \n★ Mis en avant \nVmake Video Enhancer \n« Améliorez vos vidéos floues et de mauvaise qualité pour les transformer en HD et 4K grâce à l'IA. Vmake accentue les détails de vos vidéos, améliore leur netteté et leur donne un aspect plus professionnel en quelques secondes »\nVisiter \nIA Alternatives à \nInkling \nPayant GPT-5.4 \n4.4\u002F5 \n« Le modèle frontier le plus puissant d'OpenAI contrôle directement votre ordinateur, traite 1 million de tokens et raisonne sur des tâches très longues. Annoncé comme réduisant les erreurs de 33 % et améliorant considérablement le code et l'analyse des documents »\n189 Visiter Gratuit DeepSeek V4 \n4.4\u002F5 \n« Ce modèle Mixture‑of‑Experts d’environ 1 000 milliards de paramètres offre un contexte de 1M tokens, une mémoire Engram quasi infinie et des capacités multimodales texte, image et vidéo. Il vise des scores proches de Claude Opus en code tout en restant bien moins cher ( prévus en Apache 2.0)»\n97 Visiter Freemium Kimi K3 \n4.4\u002F5 \n« Le modèle LLM de chez Moonshot qui rivalise avec les meilleurs modèles propriétaires sur de nombreux benchmarks. Conçu pour le codage longue durée, le raisonnement avancé et les tâches de knowledge work, c'est aussi le premier modèle open source à franchir la barre des 2,8 trillions de paramètres»\n242 Visiter Payant GPT‑5.6 \n4.4\u002F5 \n« Le dernier modèle d’OpenAI pour ceux qui ont besoin d’un IA plus forte en code, en science et en cybersécurité. Disponible directement dans ChatGPT ou via l'API en trois versions : Sol, Terra et Luna»\n102 Visiter Payant Claude Opus 4.8 \n4.4\u002F5 \n« Exploitez le modèle IA d'Anthropic, plus fiable, plus honnête et 4 fois moins enclin à laisser passer ses propres erreurs. Son mode rapide délivre jusqu'à 2,5x plus de tokens par seconde (au même tarif API que la version précédente)\n»\n248 Visiter Freemium GPT‑5.5 Instant \n4.4\u002F5 \n« Utilisez un ChatGPT plus fiable et plus direct. Ce nouveau modèle réduit nettement les hallucinations sur les sujets sensibles (médecine, droit, finance). Il raccourcit ses réponses et personnalise davantage selon vos fichiers, conversations passées et compte Gmail connecté.»\n89 Visiter Freemium Claude Sonnet 5 \n4.5\u002F5 \n« Un modèle LLM pensé pour agir comme un vrai collaborateur technique. Il planifie, utilise le navigateur et le terminal, gère du code sur de longs contextes et atteint des scores de niveau Opus. Il reste également moins cher et plus adapté aux agents autonomes»\n104 Visiter Payant Claude Opus 4.7 \n4.5\u002F5 \n« Le dernier modèle haut de gamme de chez Anthropic optimisé pour le code agentique, les tâches longues et les scénarios critiques en entreprise. Avec auto‑vérification des réponses et fenêtre de contexte d’un million de tokens. Il est également capable d'ajuster automatiquement la profondeur de sa réflexion selon la difficulté du problème»\n162 Visiter \n+ Voir plus d'IA \nCopier le code \nL'intelligence artificielle pour tous\nRessources Tutoriels, astuces et blog Agenda des conférences sur l'IA Glossaire des IA Emplois dans l'IA Newsletter Services pratiques Chaînes YouTube sur l'IA Top 100 IA Liste GPTs Outils IA Hubspot Meilleurs Agents IA Entreprise Ajouter un outil IA Annoncer Mettre à jour votre site Sponsorship ★ Media Kit Qui sommes-nous ? Contactez-nous \n© 2026 AIxploria. 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Follow these links to get started.","1. General Information","2. Getting Started","3. Model Properties","Model type","Architecture type","Parameters","Numerics support","Input modalities","Output modalities","4. Training","5. Evaluations","6. Safety","7. Bias, risks and limitations","Model tree for thinkingmachines\u002FInkling","Spaces using thinkingmachines\u002FInkling 17","Collection including thinkingmachines\u002FInkling","Evaluation results",[459,460,453,461,462,463,464,465,466,467],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up \\\", \\\"assistant\\\": \\\"\u003C|message_model|>\\\", \\\"system\\\": \\\"\u003C|message_system|>\\\", \\\"tool\\\": \\\"\u003C|message_tool|>\\\"} -%}\\n\\n{%- macro emit_thinking_effort() -%}\\n {%- set eff = reasoning_effort if reasoning_effort is defined and reasoning_effort is not none else 0.9 -%}\\n {%- if eff is string -%}\\n {%- set key = eff | trim -%}\\n {%- if key not in effort_map -%}\\n {{- raise_exception(\\\"Unknown reasoning_effort: \\\" ~ eff) -}}\\n {%- endif -%}\\n {%- set num = effort_map[key] -%}\\n {%- else -%}\\n {%- set num = eff | float -%}\\n {%- endif -%}\\n {%- if num \u003C 0.0 or num > 0.99 -%}\\n {{- raise_exception(\\\"reasoning_effort must be in [0.0, 0.99]\\\") -}}\\n {%- endif -%}\\n {{- \\\"\u003C|message_system|>\u003C|content_text|>Thinking effort level: \\\" -}}\\n {%- if num == 0.0 -%}0{%- else -%}{{ num }}{%- endif -%}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n{%- endmacro -%}\\n\\n{%- if tools -%}\\n {%- set tool_state = namespace(specs=[]) -%}\\n {%- for tool in tools -%}\\n {%- set fn = tool.function if tool.function is defined else tool -%}\\n {%- set spec = {\\n \\\"description\\\": (fn.description if fn.description is defined and fn.description else \\\"\\\"),\\n \\\"name\\\": fn.name,\\n \\\"parameters\\\": (fn.parameters if fn.parameters is defined and fn.parameters else {}),\\n \\\"type\\\": (tool.type if tool.type is defined and tool.type else \\\"function\\\"),\\n } -%}\\n {%- set tool_state.specs = tool_state.specs + [spec] -%}\\n {%- endfor -%}\\n {{- \\\"\u003C|message_system|>tool_declare\u003C|content_xml|>\\\" -}}\\n {{- tool_state.specs | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\")) -}}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n{%- endif -%}\\n\\n{%- set state = namespace(effort_emitted=false) -%}\\n{%- for message in messages -%}\\n {%- if message.role not in role_token -%}\\n {{- raise_exception(\\\"Unknown message role: \\\" ~ message.role) -}}\\n {%- endif -%}\\n {%- if not state.effort_emitted and message.role != \\\"system\\\" -%}\\n {{- emit_thinking_effort() -}}\\n {%- set state.effort_emitted = true -%}\\n {%- endif -%}\\n\\n {%- set rtok = role_token[message.role] -%}\\n\\n {%- if message.role == \\\"tool\\\" -%}\\n {%- set tool_name_state = namespace(name=\\\"\\\") -%}\\n {%- if message.name is defined and message.name -%}\\n {%- set tool_name_state.name = message.name -%}\\n {%- elif message.tool_call_id is defined and message.tool_call_id -%}\\n {%- for prev in messages -%}\\n {%- if prev.role == \\\"assistant\\\" and prev.tool_calls -%}\\n {%- for tc in prev.tool_calls -%}\\n {%- if tc.id is defined and tc.id == message.tool_call_id and tc.function.name is defined -%}\\n {%- set tool_name_state.name = tc.function.name -%}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n {{- rtok -}}\\n {%- if tool_name_state.name -%}{{- tool_name_state.name -}}{%- endif -%}\\n {{- \\\"\u003C|content_text|>\\\" -}}\\n {%- if message.content is string -%}{{- message.content -}}{%- endif -%}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n\\n {%- else -%}\\n {%- if message.role == \\\"assistant\\\" and message.reasoning_content is defined and message.reasoning_content -%}\\n {{- \\\"\u003C|message_model|>\u003C|content_thinking|>\\\" ~ message.reasoning_content ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- endif -%}\\n\\n {%- if message.content is string -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ message.content ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif message.content -%}\\n {%- for part in message.content -%}\\n {%- if part is string -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ part ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif part.type is not defined or part.type in (\\\"text\\\", \\\"input_text\\\") -%}\\n {%- set text_part = (part.text if part.text is defined and part.text is string else \\\"\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ text_part ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif part.type in (\\\"image\\\", \\\"input_image\\\", \\\"image_url\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_image|>\u003C|unused_200054|>\u003C|end_message|>\\\" -}}\\n {%- elif part.type in (\\\"audio\\\", \\\"input_audio\\\", \\\"audio_url\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_audio_input|>\u003C|unused_200053|>\u003C|audio_end|>\u003C|end_message|>\\\" -}}\\n {%- else -%}\\n {{- raise_exception(\\\"Unsupported content part type: \\\" ~ part.type) -}}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n\\n {%- if message.role == \\\"assistant\\\" and message.tool_calls -%}\\n {%- for tc in message.tool_calls -%}\\n {%- set fn = tc.function -%}\\n {%- if fn.name is not defined or fn.name is not string -%}\\n {{- raise_exception(\\\"tool call function name must be a string\\\") -}}\\n {%- endif -%}\\n {%- set args = fn.arguments if fn.arguments is defined and fn.arguments else {} -%}\\n {%- if args is string -%}\\n {{- raise_exception(\\\"tool call arguments must be a parsed object, not a JSON string; canonicalize upstream\\\") -}}\\n {%- endif -%}\\n {%- if args is not mapping -%}\\n {{- raise_exception(\\\"tool call arguments must be an object\\\") -}}\\n {%- endif -%}\\n {{- \\\"\u003C|message_model|>\\\" ~ fn.name ~ \\\"\u003C|content_invoke_tool_json|>\\\" -}}\\n {{- '{\\\"name\\\":' ~ (fn.name | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\"))) ~ ',\\\"args\\\":' -}}\\n {{- (args | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\"))) -}}\\n {{- \\\"}\u003C|end_message|>\\\" -}}\\n {%- endfor -%}\\n {%- endif -%}\\n\\n {%- if message.role == \\\"assistant\\\" -%}\\n {{- \\\"\u003C|content_model_end_sampling|>\\\" -}}\\n {%- endif -%}\\n {%- endif -%}\\n{%- endfor -%}\\n\\n{%- if not state.effort_emitted -%}\\n {{- emit_thinking_effort() -}}\\n{%- endif -%}\\n\\n{%- if add_generation_prompt -%}\\n {{- \\\"\u003C|message_model|>\\\" -}}\\n{%- endif -%}\\n\"},\"createdAt\":\"2026-07-14T13:23:14.000Z\",\"discussionsDisabled\":false,\"discussionsSorting\":\"recently-created\",\"downloads\":77799,\"downloadsAllTime\":77799,\"id\":\"thinkingmachines\u002FInkling\",\"isLikedByUser\":false,\"availableInferenceProviders\":[{\"provider\":\"baseten\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002Finkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":true,\"toolCalling\":true},\"isCheapestPricingOutput\":true,\"isFastestThroughput\":true,\"isModelAuthor\":false,\"tokensPerSecond\":158.72110743627016,\"pricingOutput\":4.05},{\"provider\":\"fireworks-ai\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"accounts\u002Ffireworks\u002Fmodels\u002Finkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":false,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":102.38685589674759},{\"provider\":\"together\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002FInkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":true,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":127.45434889475416,\"pricingOutput\":4.05},{\"provider\":\"deepinfra\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002FInkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":false,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":99.40961150436104,\"pricingOutput\":4.05}],\"showHuggingChatEntry\":true,\"inference\":\"warm\",\"lastModified\":\"2026-07-23T17:27:17.000Z\",\"likes\":1703,\"pipeline_tag\":\"image-text-to-text\",\"library_name\":\"transformers\",\"librariesOther\":[],\"trackDownloads\":true,\"model-index\":null,\"evalResults\":[{\"dataset\":{\"id\":\"SWE-bench\u002FSWE-bench_Verified\",\"isBenchmark\":true,\"task_id\":\"swe_bench_%_resolved\"},\"value\":77.6,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model 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Options\"}],\"private\":false,\"repoType\":\"model\",\"gated\":false,\"tags\":[\"transformers\",\"safetensors\",\"inkling_mm_model\",\"image-text-to-text\",\"conversational\",\"audio-text-to-text\",\"moe\",\"license:apache-2.0\",\"eval-results\",\"endpoints_compatible\",\"region:us\"],\"tag_objs\":[{\"id\":\"image-text-to-text\",\"label\":\"Image-Text-to-Text\",\"type\":\"pipeline_tag\",\"subType\":\"multimodal\"},{\"id\":\"transformers\",\"label\":\"Transformers\",\"type\":\"library\"},{\"id\":\"safetensors\",\"label\":\"Safetensors\",\"type\":\"library\"},{\"id\":\"inkling_mm_model\",\"label\":\"inkling_mm_model\",\"type\":\"other\",\"clickable\":true},{\"id\":\"conversational\",\"label\":\"conversational\",\"type\":\"other\",\"clickable\":true},{\"id\":\"audio-text-to-text\",\"label\":\"audio-text-to-text\",\"type\":\"other\",\"clickable\":true},{\"id\":\"moe\",\"label\":\"Mixture of Experts\",\"type\":\"other\",\"clickable\":true},{\"id\":\"eval-results\",\"label\":\"Eval Results\",\"type\":\"other\",\"clickable\":true},{\"id\":\"endpoints_compatible\",\"label\":\"Inference Endpoints\",\"type\":\"other\",\"clickable\":true},{\"id\":\"license:apache-2.0\",\"label\":\"apache-2.0\",\"type\":\"license\"},{\"type\":\"region\",\"label\":\"🇺🇸 Region: US\",\"id\":\"region:us\"}],\"transformersInfo\":{\"auto_model\":\"AutoModelForMultimodalLM\",\"pipeline_tag\":\"image-text-to-text\",\"processor\":\"AutoProcessor\"},\"widgetData\":[{\"text\":\"Hi, what can you help me with?\"},{\"text\":\"What is 84 * 3 \u002F 2?\"},{\"text\":\"Tell me an interesting fact about the universe!\"},{\"text\":\"Explain quantum computing in simple terms.\"}],\"safetensors\":{\"parameters\":{\"BF16\":952377607178,\"F32\":16448},\"total\":952377623626,\"sharded\":true,\"totalFileSize\":1904755592540},\"hasBlockedOids\":false,\"region\":\"us\",\"isQuantized\":false},\"discussionsStats\":{\"closed\":17,\"open\":0,\"total\":17},\"query\":{},\"inferenceContextData\":{\"billableEntities\":[],\"entityName2Providers\":{},\"defaultProviders\":[{\"isOriginalProvider\":false,\"name\":\"together\",\"enabled\":true,\"position\":5,\"isReleased\":true,\"accuratePricing\":true},{\"isOriginalProvider\":false,\"name\":\"fireworks-ai\",\"enabled\":true,\"position\":6,\"isReleased\":true,\"accuratePricing\":true},{\"isOriginalProvider\":false,\"name\":\"baseten\",\"enabled\":true,\"position\":13,\"isReleased\":true,\"accuratePricing\":true},{\"isOriginalProvider\":false,\"name\":\"deepinfra\",\"enabled\":true,\"position\":15,\"isReleased\":true,\"accuratePricing\":true}]},\"hasQuantizations\":true,\"copyToBucketNamespaces\":[]}\"> thinkingmachines \u002F Inkling like 1.7k Follow Thinking Machines Lab 1.65k","Quantizations to use this model in llama.cpp , Ollama , LM Studio , or any compatible app. Inkling 1. General Information 2. Getting Started 3. Model Properties Model type Architecture type Parameters Numerics support Input modalities Output modalities 4. Training 5. Evaluations 6. Safety 7. Bias, risks and limitations","Inkling may exhibit general limitations common to foundation models, including hallucination (generating plausible but factually incorrect or unsupported content), occasional failures to follow instructions precisely, and degraded performance in long multi-turn conversations. As with other large-scale models trained on web-derived and synthetic data, Inkling may reflect biases present in its training data, including demographic, cultural, or linguistic biases, and may perform unevenly across languages, dialects, or subject domains that were less represented during training.","Inkling's knowledge is limited to information available as of its training cutoff, and it may not reflect events, developments, or changes that occurred afterward.","Implement additional safeguards — such as content filtering, rate limiting, and monitoring — at the application layer, especially for open deployment contexts where Inkling's built-in mitigations may not be sufficient on their own.","\\\", \\\"assistant\\\": \\\"\u003C|message_model|>\\\", \\\"system\\\": \\\"\u003C|message_system|>\\\", \\\"tool\\\": \\\"\u003C|message_tool|>\\\"} -%}\\n\\n{%- macro emit_thinking_effort() -%}\\n {%- set eff = reasoning_effort if reasoning_effort is defined and reasoning_effort is not none else 0.9 -%}\\n {%- if eff is string -%}\\n {%- set key = eff | trim -%}\\n {%- if key not in effort_map -%}\\n {{- raise_exception(\\\"Unknown reasoning_effort: \\\" ~ eff) -}}\\n {%- endif -%}\\n {%- set num = effort_map[key] -%}\\n {%- else -%}\\n {%- set num = eff | float -%}\\n {%- endif -%}\\n {%- if num \u003C 0.0 or num > 0.99 -%}\\n {{- raise_exception(\\\"reasoning_effort must be in [0.0, 0.99]\\\") -}}\\n {%- endif -%}\\n {{- \\\"\u003C|message_system|>\u003C|content_text|>Thinking effort level: \\\" -}}\\n {%- if num == 0.0 -%}0{%- else -%}{{ num }}{%- endif -%}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n{%- endmacro -%}\\n\\n{%- if tools -%}\\n {%- set tool_state = namespace(specs=[]) -%}\\n {%- for tool in tools -%}\\n {%- set fn = tool.function if tool.function is defined else tool -%}\\n {%- set spec = {\\n \\\"description\\\": (fn.description if fn.description is defined and fn.description else \\\"\\\"),\\n \\\"name\\\": fn.name,\\n \\\"parameters\\\": (fn.parameters if fn.parameters is defined and fn.parameters else {}),\\n \\\"type\\\": (tool.type if tool.type is defined and tool.type else \\\"function\\\"),\\n } -%}\\n {%- set tool_state.specs = tool_state.specs + [spec] -%}\\n {%- endfor -%}\\n {{- \\\"\u003C|message_system|>tool_declare\u003C|content_xml|>\\\" -}}\\n {{- tool_state.specs | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\")) -}}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n{%- endif -%}\\n\\n{%- set state = namespace(effort_emitted=false) -%}\\n{%- for message in messages -%}\\n {%- if message.role not in role_token -%}\\n {{- raise_exception(\\\"Unknown message role: \\\" ~ message.role) -}}\\n {%- endif -%}\\n {%- if not state.effort_emitted and message.role != \\\"system\\\" -%}\\n {{- emit_thinking_effort() -}}\\n {%- set state.effort_emitted = true -%}\\n {%- endif -%}\\n\\n {%- set rtok = role_token[message.role] -%}\\n\\n {%- if message.role == \\\"tool\\\" -%}\\n {%- set tool_name_state = namespace(name=\\\"\\\") -%}\\n {%- if message.name is defined and message.name -%}\\n {%- set tool_name_state.name = message.name -%}\\n {%- elif message.tool_call_id is defined and message.tool_call_id -%}\\n {%- for prev in messages -%}\\n {%- if prev.role == \\\"assistant\\\" and prev.tool_calls -%}\\n {%- for tc in prev.tool_calls -%}\\n {%- if tc.id is defined and tc.id == message.tool_call_id and tc.function.name is defined -%}\\n {%- set tool_name_state.name = tc.function.name -%}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n {{- rtok -}}\\n {%- if tool_name_state.name -%}{{- tool_name_state.name -}}{%- endif -%}\\n {{- \\\"\u003C|content_text|>\\\" -}}\\n {%- if message.content is string -%}{{- message.content -}}{%- endif -%}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n\\n {%- else -%}\\n {%- if message.role == \\\"assistant\\\" and message.reasoning_content is defined and message.reasoning_content -%}\\n {{- \\\"\u003C|message_model|>\u003C|content_thinking|>\\\" ~ message.reasoning_content ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- endif -%}\\n\\n {%- if message.content is string -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ message.content ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif message.content -%}\\n {%- for part in message.content -%}\\n {%- if part is string -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ part ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif part.type is not defined or part.type in (\\\"text\\\", \\\"input_text\\\") -%}\\n {%- set text_part = (part.text if part.text is defined and part.text is string else \\\"\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ text_part ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif part.type in (\\\"image\\\", \\\"input_image\\\", \\\"image_url\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_image|>\u003C|unused_200054|>\u003C|end_message|>\\\" -}}\\n {%- elif part.type in (\\\"audio\\\", \\\"input_audio\\\", \\\"audio_url\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_audio_input|>\u003C|unused_200053|>\u003C|audio_end|>\u003C|end_message|>\\\" -}}\\n {%- else -%}\\n {{- raise_exception(\\\"Unsupported content part type: \\\" ~ part.type) -}}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n\\n {%- if message.role == \\\"assistant\\\" and message.tool_calls -%}\\n {%- for tc in message.tool_calls -%}\\n {%- set fn = tc.function -%}\\n {%- if fn.name is not defined or fn.name is not string -%}\\n {{- raise_exception(\\\"tool call function name must be a string\\\") -}}\\n {%- endif -%}\\n {%- set args = fn.arguments if fn.arguments is defined and fn.arguments else {} -%}\\n {%- if args is string -%}\\n {{- raise_exception(\\\"tool call arguments must be a parsed object, not a JSON string; canonicalize upstream\\\") -}}\\n {%- endif -%}\\n {%- if args is not mapping -%}\\n {{- raise_exception(\\\"tool call arguments must be an object\\\") -}}\\n {%- endif -%}\\n {{- \\\"\u003C|message_model|>\\\" ~ fn.name ~ \\\"\u003C|content_invoke_tool_json|>\\\" -}}\\n {{- '{\\\"name\\\":' ~ (fn.name | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\"))) ~ ',\\\"args\\\":' -}}\\n {{- (args | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\"))) -}}\\n {{- \\\"}\u003C|end_message|>\\\" -}}\\n {%- endfor -%}\\n {%- endif -%}\\n\\n {%- if message.role == \\\"assistant\\\" -%}\\n {{- \\\"\u003C|content_model_end_sampling|>\\\" -}}\\n {%- endif -%}\\n {%- endif -%}\\n{%- endfor -%}\\n\\n{%- if not state.effort_emitted -%}\\n {{- emit_thinking_effort() -}}\\n{%- endif -%}\\n\\n{%- if add_generation_prompt -%}\\n {{- \\\"\u003C|message_model|>\\\" -}}\\n{%- endif -%}\\n\"},\"createdAt\":\"2026-07-14T13:23:14.000Z\",\"discussionsDisabled\":false,\"discussionsSorting\":\"recently-created\",\"downloads\":77799,\"downloadsAllTime\":77799,\"id\":\"thinkingmachines\u002FInkling\",\"isLikedByUser\":false,\"availableInferenceProviders\":[{\"provider\":\"baseten\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002Finkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":true,\"toolCalling\":true},\"isCheapestPricingOutput\":true,\"isFastestThroughput\":true,\"isModelAuthor\":false,\"tokensPerSecond\":158.72110743627016,\"pricingOutput\":4.05},{\"provider\":\"fireworks-ai\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"accounts\u002Ffireworks\u002Fmodels\u002Finkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":false,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":102.38685589674759},{\"provider\":\"together\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002FInkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":true,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":127.45434889475416,\"pricingOutput\":4.05},{\"provider\":\"deepinfra\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002FInkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":false,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":99.40961150436104,\"pricingOutput\":4.05}],\"showHuggingChatEntry\":true,\"inference\":\"warm\",\"lastModified\":\"2026-07-23T17:27:17.000Z\",\"likes\":1703,\"pipeline_tag\":\"image-text-to-text\",\"library_name\":\"transformers\",\"librariesOther\":[],\"trackDownloads\":true,\"model-index\":null,\"evalResults\":[{\"dataset\":{\"id\":\"SWE-bench\u002FSWE-bench_Verified\",\"isBenchmark\":true,\"task_id\":\"swe_bench_%_resolved\"},\"value\":77.6,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fswe-bench_verified.yaml\",\"verified\":false,\"rank\":10,\"label\":\"Swe Bench Resolved\"},{\"dataset\":{\"id\":\"ScaleAI\u002FSWE-bench_Pro\",\"isBenchmark\":true,\"task_id\":\"SWE_Bench_Pro\"},\"value\":54.3,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fswe-bench_pro.yaml\",\"verified\":false,\"rank\":16,\"label\":\"SWE Bench Pro\"},{\"dataset\":{\"id\":\"Idavidrein\u002Fgpqa\",\"isBenchmark\":true,\"task_id\":\"diamond\"},\"value\":87.2,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model 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Options\"}],\"private\":false,\"repoType\":\"model\",\"gated\":false,\"tags\":[\"transformers\",\"safetensors\",\"inkling_mm_model\",\"image-text-to-text\",\"conversational\",\"audio-text-to-text\",\"moe\",\"license:apache-2.0\",\"eval-results\",\"endpoints_compatible\",\"region:us\"],\"tag_objs\":[{\"id\":\"image-text-to-text\",\"label\":\"Image-Text-to-Text\",\"type\":\"pipeline_tag\",\"subType\":\"multimodal\"},{\"id\":\"transformers\",\"label\":\"Transformers\",\"type\":\"library\"},{\"id\":\"safetensors\",\"label\":\"Safetensors\",\"type\":\"library\"},{\"id\":\"inkling_mm_model\",\"label\":\"inkling_mm_model\",\"type\":\"other\",\"clickable\":true},{\"id\":\"conversational\",\"label\":\"conversational\",\"type\":\"other\",\"clickable\":true},{\"id\":\"audio-text-to-text\",\"label\":\"audio-text-to-text\",\"type\":\"other\",\"clickable\":true},{\"id\":\"moe\",\"label\":\"Mixture of Experts\",\"type\":\"other\",\"clickable\":true},{\"id\":\"eval-results\",\"label\":\"Eval Results\",\"type\":\"other\",\"clickable\":true},{\"id\":\"endpoints_compatible\",\"label\":\"Inference Endpoints\",\"type\":\"other\",\"clickable\":true},{\"id\":\"license:apache-2.0\",\"label\":\"apache-2.0\",\"type\":\"license\"},{\"type\":\"region\",\"label\":\"🇺🇸 Region: US\",\"id\":\"region:us\"}],\"transformersInfo\":{\"auto_model\":\"AutoModelForMultimodalLM\",\"pipeline_tag\":\"image-text-to-text\",\"processor\":\"AutoProcessor\"},\"widgetData\":[{\"text\":\"Hi, what can you help me with?\"},{\"text\":\"What is 84 * 3 \u002F 2?\"},{\"text\":\"Tell me an interesting fact about the universe!\"},{\"text\":\"Explain quantum computing in simple terms.\"}],\"safetensors\":{\"parameters\":{\"BF16\":952377607178,\"F32\":16448},\"total\":952377623626,\"sharded\":true,\"totalFileSize\":1904755592540},\"hasBlockedOids\":false,\"region\":\"us\",\"isQuantized\":false},\"inferenceContextData\":{\"billableEntities\":[],\"entityName2Providers\":{},\"defaultProviders\":[{\"isOriginalProvider\":false,\"name\":\"together\",\"enabled\":true,\"position\":5,\"isReleased\":true,\"accuratePricing\":true},{\"isOriginalProvider\":false,\"name\":\"fireworks-ai\",\"enabled\":true,\"position\":6,\"isReleased\":true,\"accuratePricing\":true},{\"isOriginalProvider\":false,\"name\":\"baseten\",\"enabled\":true,\"position\":13,\"isReleased\":true,\"accuratePricing\":true},{\"isOriginalProvider\":false,\"name\":\"deepinfra\",\"enabled\":true,\"position\":15,\"isReleased\":true,\"accuratePricing\":true}]},\"canWrite\":false}\"> Safetensors Model size 952B params Tensor type BF16 · F32 · Chat template","\\\", \\\"assistant\\\": \\\"\u003C|message_model|>\\\", \\\"system\\\": \\\"\u003C|message_system|>\\\", \\\"tool\\\": \\\"\u003C|message_tool|>\\\"} -%}\\n\\n{%- macro emit_thinking_effort() -%}\\n {%- set eff = reasoning_effort if reasoning_effort is defined and reasoning_effort is not none else 0.9 -%}\\n {%- if eff is string -%}\\n {%- set key = eff | trim -%}\\n {%- if key not in effort_map -%}\\n {{- raise_exception(\\\"Unknown reasoning_effort: \\\" ~ eff) -}}\\n {%- endif -%}\\n {%- set num = effort_map[key] -%}\\n {%- else -%}\\n {%- set num = eff | float -%}\\n {%- endif -%}\\n {%- if num \u003C 0.0 or num > 0.99 -%}\\n {{- raise_exception(\\\"reasoning_effort must be in [0.0, 0.99]\\\") -}}\\n {%- endif -%}\\n {{- \\\"\u003C|message_system|>\u003C|content_text|>Thinking effort level: \\\" -}}\\n {%- if num == 0.0 -%}0{%- else -%}{{ num }}{%- endif -%}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n{%- endmacro -%}\\n\\n{%- if tools -%}\\n {%- set tool_state = namespace(specs=[]) -%}\\n {%- for tool in tools -%}\\n {%- set fn = tool.function if tool.function is defined else tool -%}\\n {%- set spec = {\\n \\\"description\\\": (fn.description if fn.description is defined and fn.description else \\\"\\\"),\\n \\\"name\\\": fn.name,\\n \\\"parameters\\\": (fn.parameters if fn.parameters is defined and fn.parameters else {}),\\n \\\"type\\\": (tool.type if tool.type is defined and tool.type else \\\"function\\\"),\\n } -%}\\n {%- set tool_state.specs = tool_state.specs + [spec] -%}\\n {%- endfor -%}\\n {{- \\\"\u003C|message_system|>tool_declare\u003C|content_xml|>\\\" -}}\\n {{- tool_state.specs | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\")) -}}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n{%- endif -%}\\n\\n{%- set state = namespace(effort_emitted=false) -%}\\n{%- for message in messages -%}\\n {%- if message.role not in role_token -%}\\n {{- raise_exception(\\\"Unknown message role: \\\" ~ message.role) -}}\\n {%- endif -%}\\n {%- if not state.effort_emitted and message.role != \\\"system\\\" -%}\\n {{- emit_thinking_effort() -}}\\n {%- set state.effort_emitted = true -%}\\n {%- endif -%}\\n\\n {%- set rtok = role_token[message.role] -%}\\n\\n {%- if message.role == \\\"tool\\\" -%}\\n {%- set tool_name_state = namespace(name=\\\"\\\") -%}\\n {%- if message.name is defined and message.name -%}\\n {%- set tool_name_state.name = message.name -%}\\n {%- elif message.tool_call_id is defined and message.tool_call_id -%}\\n {%- for prev in messages -%}\\n {%- if prev.role == \\\"assistant\\\" and prev.tool_calls -%}\\n {%- for tc in prev.tool_calls -%}\\n {%- if tc.id is defined and tc.id == message.tool_call_id and tc.function.name is defined -%}\\n {%- set tool_name_state.name = tc.function.name -%}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n {{- rtok -}}\\n {%- if tool_name_state.name -%}{{- tool_name_state.name -}}{%- endif -%}\\n {{- \\\"\u003C|content_text|>\\\" -}}\\n {%- if message.content is string -%}{{- message.content -}}{%- endif -%}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n\\n {%- else -%}\\n {%- if message.role == \\\"assistant\\\" and message.reasoning_content is defined and message.reasoning_content -%}\\n {{- \\\"\u003C|message_model|>\u003C|content_thinking|>\\\" ~ message.reasoning_content ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- endif -%}\\n\\n {%- if message.content is string -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ message.content ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif message.content -%}\\n {%- for part in message.content -%}\\n {%- if part is string -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ part ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif part.type is not defined or part.type in (\\\"text\\\", \\\"input_text\\\") -%}\\n {%- set text_part = (part.text if part.text is defined and part.text is string else \\\"\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ text_part ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif part.type in (\\\"image\\\", \\\"input_image\\\", \\\"image_url\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_image|>\u003C|unused_200054|>\u003C|end_message|>\\\" -}}\\n {%- elif part.type in (\\\"audio\\\", \\\"input_audio\\\", \\\"audio_url\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_audio_input|>\u003C|unused_200053|>\u003C|audio_end|>\u003C|end_message|>\\\" -}}\\n {%- else -%}\\n {{- raise_exception(\\\"Unsupported content part type: \\\" ~ part.type) -}}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n\\n {%- if message.role == \\\"assistant\\\" and message.tool_calls -%}\\n {%- for tc in message.tool_calls -%}\\n {%- set fn = tc.function -%}\\n {%- if fn.name is not defined or fn.name is not string -%}\\n {{- raise_exception(\\\"tool call function name must be a string\\\") -}}\\n {%- endif -%}\\n {%- set args = fn.arguments if fn.arguments is defined and fn.arguments else {} -%}\\n {%- if args is string -%}\\n {{- raise_exception(\\\"tool call arguments must be a parsed object, not a JSON string; canonicalize upstream\\\") -}}\\n {%- endif -%}\\n {%- if args is not mapping -%}\\n {{- raise_exception(\\\"tool call arguments must be an object\\\") -}}\\n {%- endif -%}\\n {{- \\\"\u003C|message_model|>\\\" ~ fn.name ~ \\\"\u003C|content_invoke_tool_json|>\\\" -}}\\n {{- '{\\\"name\\\":' ~ (fn.name | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\"))) ~ ',\\\"args\\\":' -}}\\n {{- (args | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\"))) -}}\\n {{- \\\"}\u003C|end_message|>\\\" -}}\\n {%- endfor -%}\\n {%- endif -%}\\n\\n {%- if message.role == \\\"assistant\\\" -%}\\n {{- \\\"\u003C|content_model_end_sampling|>\\\" -}}\\n {%- endif -%}\\n {%- endif -%}\\n{%- endfor -%}\\n\\n{%- if not state.effort_emitted -%}\\n {{- emit_thinking_effort() -}}\\n{%- endif -%}\\n\\n{%- if add_generation_prompt -%}\\n {{- \\\"\u003C|message_model|>\\\" -}}\\n{%- endif -%}\\n\"},\"createdAt\":\"2026-07-14T13:23:14.000Z\",\"discussionsDisabled\":false,\"discussionsSorting\":\"recently-created\",\"downloads\":77799,\"downloadsAllTime\":77799,\"id\":\"thinkingmachines\u002FInkling\",\"isLikedByUser\":false,\"availableInferenceProviders\":[{\"provider\":\"baseten\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002Finkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":true,\"toolCalling\":true},\"isCheapestPricingOutput\":true,\"isFastestThroughput\":true,\"isModelAuthor\":false,\"tokensPerSecond\":158.72110743627016,\"pricingOutput\":4.05},{\"provider\":\"fireworks-ai\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"accounts\u002Ffireworks\u002Fmodels\u002Finkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":false,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":102.38685589674759},{\"provider\":\"together\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002FInkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":true,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":127.45434889475416,\"pricingOutput\":4.05},{\"provider\":\"deepinfra\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002FInkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":false,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":99.40961150436104,\"pricingOutput\":4.05}],\"showHuggingChatEntry\":true,\"inference\":\"warm\",\"lastModified\":\"2026-07-23T17:27:17.000Z\",\"likes\":1703,\"pipeline_tag\":\"image-text-to-text\",\"library_name\":\"transformers\",\"librariesOther\":[],\"trackDownloads\":true,\"model-index\":null,\"evalResults\":[{\"dataset\":{\"id\":\"SWE-bench\u002FSWE-bench_Verified\",\"isBenchmark\":true,\"task_id\":\"swe_bench_%_resolved\"},\"value\":77.6,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fswe-bench_verified.yaml\",\"verified\":false,\"rank\":10,\"label\":\"Swe Bench Resolved\"},{\"dataset\":{\"id\":\"ScaleAI\u002FSWE-bench_Pro\",\"isBenchmark\":true,\"task_id\":\"SWE_Bench_Pro\"},\"value\":54.3,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fswe-bench_pro.yaml\",\"verified\":false,\"rank\":16,\"label\":\"SWE Bench Pro\"},{\"dataset\":{\"id\":\"Idavidrein\u002Fgpqa\",\"isBenchmark\":true,\"task_id\":\"diamond\"},\"value\":87.2,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fgpqa.yaml\",\"verified\":false,\"rank\":19,\"label\":\"Diamond\"},{\"dataset\":{\"id\":\"MathArena\u002Faime_2026\",\"isBenchmark\":true,\"task_id\":\"MathArena\u002Faime_2026\"},\"value\":97.1,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Faime_2026.yaml\",\"verified\":false,\"rank\":1,\"label\":\"MathArena Aime 2026\"},{\"dataset\":{\"id\":\"cais\u002Fhle\",\"isBenchmark\":false,\"task_id\":\"hle\"},\"value\":46,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model 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Options\"}],\"private\":false,\"repoType\":\"model\",\"gated\":false,\"tags\":[\"transformers\",\"safetensors\",\"inkling_mm_model\",\"image-text-to-text\",\"conversational\",\"audio-text-to-text\",\"moe\",\"license:apache-2.0\",\"eval-results\",\"endpoints_compatible\",\"region:us\"],\"tag_objs\":[{\"id\":\"image-text-to-text\",\"label\":\"Image-Text-to-Text\",\"type\":\"pipeline_tag\",\"subType\":\"multimodal\"},{\"id\":\"transformers\",\"label\":\"Transformers\",\"type\":\"library\"},{\"id\":\"safetensors\",\"label\":\"Safetensors\",\"type\":\"library\"},{\"id\":\"inkling_mm_model\",\"label\":\"inkling_mm_model\",\"type\":\"other\",\"clickable\":true},{\"id\":\"conversational\",\"label\":\"conversational\",\"type\":\"other\",\"clickable\":true},{\"id\":\"audio-text-to-text\",\"label\":\"audio-text-to-text\",\"type\":\"other\",\"clickable\":true},{\"id\":\"moe\",\"label\":\"Mixture of Experts\",\"type\":\"other\",\"clickable\":true},{\"id\":\"eval-results\",\"label\":\"Eval Results\",\"type\":\"other\",\"clickable\":true},{\"id\":\"endpoints_compatible\",\"label\":\"Inference Endpoints\",\"type\":\"other\",\"clickable\":true},{\"id\":\"license:apache-2.0\",\"label\":\"apache-2.0\",\"type\":\"license\"},{\"type\":\"region\",\"label\":\"🇺🇸 Region: US\",\"id\":\"region:us\"}],\"transformersInfo\":{\"auto_model\":\"AutoModelForMultimodalLM\",\"pipeline_tag\":\"image-text-to-text\",\"processor\":\"AutoProcessor\"},\"widgetData\":[{\"text\":\"Hi, what can you help me with?\"},{\"text\":\"What is 84 * 3 \u002F 2?\"},{\"text\":\"Tell me an interesting fact about the universe!\"},{\"text\":\"Explain quantum computing in simple terms.\"}],\"safetensors\":{\"parameters\":{\"BF16\":952377607178,\"F32\":16448},\"total\":952377623626,\"sharded\":true,\"totalFileSize\":1904755592540},\"hasBlockedOids\":false,\"region\":\"us\",\"isQuantized\":false},\"queryParams\":{},\"inferenceContextData\":{\"billableEntities\":[],\"entityName2Providers\":{},\"defaultProviders\":[{\"isOriginalProvider\":false,\"name\":\"together\",\"enabled\":true,\"position\":5,\"isReleased\":true,\"accuratePricing\":true},{\"isOriginalProvider\":false,\"name\":\"fireworks-ai\",\"enabled\":true,\"position\":6,\"isReleased\":true,\"accuratePricing\":true},{\"isOriginalProvider\":false,\"name\":\"baseten\",\"enabled\":true,\"position\":13,\"isReleased\":true,\"accuratePricing\":true},{\"isOriginalProvider\":false,\"name\":\"deepinfra\",\"enabled\":true,\"position\":15,\"isReleased\":true,\"accuratePricing\":true}]},\"linkToInferenceDiscussion\":{\"url\":\"\u002Fspaces\u002Fhuggingface\u002FInferenceSupport\u002Fdiscussions\u002F11084\",\"numReactions\":56},\"isLoggedIn\":false,\"canWrite\":false,\"examples\":[{\"text\":\"Hi, what can you help me with?\"},{\"text\":\"What is 84 * 3 \u002F 2?\"},{\"text\":\"Tell me an interesting fact about the universe!\"},{\"text\":\"Explain quantum computing in simple terms.\"}],\"initialSelectedProvider\":\"together\",\"widgetType\":\"conversational\",\"isMaximized\":false,\"isMac\":false}}\"> Inference Providers NEW Together AI +1 Image-Text-to-Text Examples Input a message to start chatting with thinkingmachines\u002FInkling . Send View Code Snippets Compare providers Model tree for thinkingmachines\u002FInkling","Collection Inkling is a versatile, customizable model that reasons over text, images, audio, with variable and efficient thinking effort. • 4 items • Updated 12 days ago • 47 \\\", \\\"assistant\\\": \\\"\u003C|message_model|>\\\", \\\"system\\\": \\\"\u003C|message_system|>\\\", \\\"tool\\\": \\\"\u003C|message_tool|>\\\"} -%}\\n\\n{%- macro emit_thinking_effort() -%}\\n {%- set eff = reasoning_effort if reasoning_effort is defined and reasoning_effort is not none else 0.9 -%}\\n {%- if eff is string -%}\\n {%- set key = eff | trim -%}\\n {%- if key not in effort_map -%}\\n {{- raise_exception(\\\"Unknown reasoning_effort: \\\" ~ eff) -}}\\n {%- endif -%}\\n {%- set num = effort_map[key] -%}\\n {%- else -%}\\n {%- set num = eff | float -%}\\n {%- endif -%}\\n {%- if num \u003C 0.0 or num > 0.99 -%}\\n {{- raise_exception(\\\"reasoning_effort must be in [0.0, 0.99]\\\") -}}\\n {%- endif -%}\\n {{- \\\"\u003C|message_system|>\u003C|content_text|>Thinking effort level: \\\" -}}\\n {%- if num == 0.0 -%}0{%- else -%}{{ num }}{%- endif -%}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n{%- endmacro -%}\\n\\n{%- if tools -%}\\n {%- set tool_state = namespace(specs=[]) -%}\\n {%- for tool in tools -%}\\n {%- set fn = tool.function if tool.function is defined else tool -%}\\n {%- set spec = {\\n \\\"description\\\": (fn.description if fn.description is defined and fn.description else \\\"\\\"),\\n \\\"name\\\": fn.name,\\n \\\"parameters\\\": (fn.parameters if fn.parameters is defined and fn.parameters else {}),\\n \\\"type\\\": (tool.type if tool.type is defined and tool.type else \\\"function\\\"),\\n } -%}\\n {%- set tool_state.specs = tool_state.specs + [spec] -%}\\n {%- endfor -%}\\n {{- \\\"\u003C|message_system|>tool_declare\u003C|content_xml|>\\\" -}}\\n {{- tool_state.specs | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\")) -}}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n{%- endif -%}\\n\\n{%- set state = namespace(effort_emitted=false) -%}\\n{%- for message in messages -%}\\n {%- if message.role not in role_token -%}\\n {{- raise_exception(\\\"Unknown message role: \\\" ~ message.role) -}}\\n {%- endif -%}\\n {%- if not state.effort_emitted and message.role != \\\"system\\\" -%}\\n {{- emit_thinking_effort() -}}\\n {%- set state.effort_emitted = true -%}\\n {%- endif -%}\\n\\n {%- set rtok = role_token[message.role] -%}\\n\\n {%- if message.role == \\\"tool\\\" -%}\\n {%- set tool_name_state = namespace(name=\\\"\\\") -%}\\n {%- if message.name is defined and message.name -%}\\n {%- set tool_name_state.name = message.name -%}\\n {%- elif message.tool_call_id is defined and message.tool_call_id -%}\\n {%- for prev in messages -%}\\n {%- if prev.role == \\\"assistant\\\" and prev.tool_calls -%}\\n {%- for tc in prev.tool_calls -%}\\n {%- if tc.id is defined and tc.id == message.tool_call_id and tc.function.name is defined -%}\\n {%- set tool_name_state.name = tc.function.name -%}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n {{- rtok -}}\\n {%- if tool_name_state.name -%}{{- tool_name_state.name -}}{%- endif -%}\\n {{- \\\"\u003C|content_text|>\\\" -}}\\n {%- if message.content is string -%}{{- message.content -}}{%- endif -%}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n\\n {%- else -%}\\n {%- if message.role == \\\"assistant\\\" and message.reasoning_content is defined and message.reasoning_content -%}\\n {{- \\\"\u003C|message_model|>\u003C|content_thinking|>\\\" ~ message.reasoning_content ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- endif -%}\\n\\n {%- if message.content is string -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ message.content ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif message.content -%}\\n {%- for part in message.content -%}\\n {%- if part is string -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ part ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif part.type is not defined or part.type in (\\\"text\\\", \\\"input_text\\\") -%}\\n {%- set text_part = (part.text if part.text is defined and part.text is string else \\\"\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ text_part ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif part.type in (\\\"image\\\", \\\"input_image\\\", \\\"image_url\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_image|>\u003C|unused_200054|>\u003C|end_message|>\\\" -}}\\n {%- elif part.type in (\\\"audio\\\", \\\"input_audio\\\", \\\"audio_url\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_audio_input|>\u003C|unused_200053|>\u003C|audio_end|>\u003C|end_message|>\\\" -}}\\n {%- else -%}\\n {{- raise_exception(\\\"Unsupported content part type: \\\" ~ part.type) -}}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n\\n {%- if message.role == \\\"assistant\\\" and message.tool_calls -%}\\n {%- for tc in message.tool_calls -%}\\n {%- set fn = tc.function -%}\\n {%- if fn.name is not defined or fn.name is not string -%}\\n {{- raise_exception(\\\"tool call function name must be a string\\\") -}}\\n {%- endif -%}\\n {%- set args = fn.arguments if fn.arguments is defined and fn.arguments else {} -%}\\n {%- if args is string -%}\\n {{- raise_exception(\\\"tool call arguments must be a parsed object, not a JSON string; canonicalize upstream\\\") -}}\\n {%- endif -%}\\n {%- if args is not mapping -%}\\n {{- raise_exception(\\\"tool call arguments must be an object\\\") -}}\\n {%- endif -%}\\n {{- \\\"\u003C|message_model|>\\\" ~ fn.name ~ \\\"\u003C|content_invoke_tool_json|>\\\" -}}\\n {{- '{\\\"name\\\":' ~ (fn.name | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\"))) ~ ',\\\"args\\\":' -}}\\n {{- (args | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\"))) -}}\\n {{- \\\"}\u003C|end_message|>\\\" -}}\\n {%- endfor -%}\\n {%- endif -%}\\n\\n {%- if message.role == \\\"assistant\\\" -%}\\n {{- \\\"\u003C|content_model_end_sampling|>\\\" -}}\\n {%- endif -%}\\n {%- endif -%}\\n{%- endfor -%}\\n\\n{%- if not state.effort_emitted -%}\\n {{- emit_thinking_effort() -}}\\n{%- endif -%}\\n\\n{%- if add_generation_prompt -%}\\n {{- \\\"\u003C|message_model|>\\\" -}}\\n{%- endif -%}\\n\"},\"createdAt\":\"2026-07-14T13:23:14.000Z\",\"discussionsDisabled\":false,\"discussionsSorting\":\"recently-created\",\"downloads\":77799,\"downloadsAllTime\":77799,\"id\":\"thinkingmachines\u002FInkling\",\"isLikedByUser\":false,\"availableInferenceProviders\":[{\"provider\":\"baseten\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002Finkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":true,\"toolCalling\":true},\"isCheapestPricingOutput\":true,\"isFastestThroughput\":true,\"isModelAuthor\":false,\"tokensPerSecond\":158.72110743627016,\"pricingOutput\":4.05},{\"provider\":\"fireworks-ai\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"accounts\u002Ffireworks\u002Fmodels\u002Finkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":false,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":102.38685589674759},{\"provider\":\"together\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002FInkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":true,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":127.45434889475416,\"pricingOutput\":4.05},{\"provider\":\"deepinfra\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002FInkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":false,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":99.40961150436104,\"pricingOutput\":4.05}],\"showHuggingChatEntry\":true,\"inference\":\"warm\",\"lastModified\":\"2026-07-23T17:27:17.000Z\",\"likes\":1703,\"pipeline_tag\":\"image-text-to-text\",\"library_name\":\"transformers\",\"librariesOther\":[],\"trackDownloads\":true,\"model-index\":null,\"evalResults\":[{\"dataset\":{\"id\":\"SWE-bench\u002FSWE-bench_Verified\",\"isBenchmark\":true,\"task_id\":\"swe_bench_%_resolved\"},\"value\":77.6,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fswe-bench_verified.yaml\",\"verified\":false,\"rank\":10,\"label\":\"Swe Bench Resolved\"},{\"dataset\":{\"id\":\"ScaleAI\u002FSWE-bench_Pro\",\"isBenchmark\":true,\"task_id\":\"SWE_Bench_Pro\"},\"value\":54.3,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fswe-bench_pro.yaml\",\"verified\":false,\"rank\":16,\"label\":\"SWE Bench Pro\"},{\"dataset\":{\"id\":\"Idavidrein\u002Fgpqa\",\"isBenchmark\":true,\"task_id\":\"diamond\"},\"value\":87.2,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fgpqa.yaml\",\"verified\":false,\"rank\":19,\"label\":\"Diamond\"},{\"dataset\":{\"id\":\"MathArena\u002Faime_2026\",\"isBenchmark\":true,\"task_id\":\"MathArena\u002Faime_2026\"},\"value\":97.1,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Faime_2026.yaml\",\"verified\":false,\"rank\":1,\"label\":\"MathArena Aime 2026\"},{\"dataset\":{\"id\":\"cais\u002Fhle\",\"isBenchmark\":false,\"task_id\":\"hle\"},\"value\":46,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fhle.yaml\",\"verified\":false,\"rank\":14,\"label\":\"Hle\"},{\"dataset\":{\"id\":\"MMMU\u002FMMMU_Pro\",\"isBenchmark\":true,\"task_id\":\"mmmu_pro_standard_10_options\"},\"value\":73.5,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fmmmu_pro.yaml\",\"verified\":false,\"rank\":3,\"label\":\"Mmmu Pro Standard 10 Options\"}],\"private\":false,\"repoType\":\"model\",\"gated\":false,\"tags\":[\"transformers\",\"safetensors\",\"inkling_mm_model\",\"image-text-to-text\",\"conversational\",\"audio-text-to-text\",\"moe\",\"license:apache-2.0\",\"eval-results\",\"endpoints_compatible\",\"region:us\"],\"tag_objs\":[{\"id\":\"image-text-to-text\",\"label\":\"Image-Text-to-Text\",\"type\":\"pipeline_tag\",\"subType\":\"multimodal\"},{\"id\":\"transformers\",\"label\":\"Transformers\",\"type\":\"library\"},{\"id\":\"safetensors\",\"label\":\"Safetensors\",\"type\":\"library\"},{\"id\":\"inkling_mm_model\",\"label\":\"inkling_mm_model\",\"type\":\"other\",\"clickable\":true},{\"id\":\"conversational\",\"label\":\"conversational\",\"type\":\"other\",\"clickable\":true},{\"id\":\"audio-text-to-text\",\"label\":\"audio-text-to-text\",\"type\":\"other\",\"clickable\":true},{\"id\":\"moe\",\"label\":\"Mixture of Experts\",\"type\":\"other\",\"clickable\":true},{\"id\":\"eval-results\",\"label\":\"Eval Results\",\"type\":\"other\",\"clickable\":true},{\"id\":\"endpoints_compatible\",\"label\":\"Inference Endpoints\",\"type\":\"other\",\"clickable\":true},{\"id\":\"license:apache-2.0\",\"label\":\"apache-2.0\",\"type\":\"license\"},{\"type\":\"region\",\"label\":\"🇺🇸 Region: US\",\"id\":\"region:us\"}],\"transformersInfo\":{\"auto_model\":\"AutoModelForMultimodalLM\",\"pipeline_tag\":\"image-text-to-text\",\"processor\":\"AutoProcessor\"},\"widgetData\":[{\"text\":\"Hi, what can you help me with?\"},{\"text\":\"What is 84 * 3 \u002F 2?\"},{\"text\":\"Tell me an interesting fact about the universe!\"},{\"text\":\"Explain quantum computing in simple terms.\"}],\"safetensors\":{\"parameters\":{\"BF16\":952377607178,\"F32\":16448},\"total\":952377623626,\"sharded\":true,\"totalFileSize\":1904755592540},\"hasBlockedOids\":false,\"region\":\"us\",\"isQuantized\":false}}\"> Evaluation results","SWE-bench\u002FSWE-bench_Verified · Swe Bench Resolved View evaluation results leaderboard 77.6 ScaleAI\u002FSWE-bench_Pro · SWE Bench Pro View evaluation results leaderboard 54.3 Idavidrein\u002Fgpqa · Diamond View evaluation results leaderboard 87.2 MathArena\u002Faime_2026 · MathArena Aime 2026 View evaluation results leaderboard 97.1 cais\u002Fhle · Hle View evaluation results 46 MMMU\u002FMMMU_Pro · Mmmu Pro Standard 10 Options View evaluation results leaderboard 73.5 System theme Company TOS Privacy About Careers Website Models Datasets Spaces Pricing Docs",[459,469,470,460,450,471,472,461,462,464,465,466,467],"processor = AutoProcessor.from_pretrained(\"thinkingmachines\u002FInkling\")","model = AutoModelForMultimodalLM.from_pretrained(\"thinkingmachines\u002FInkling\", device_map=\"auto\")","Training data includes a broad variety of content types, including text, images, audio, video. Training data for the model was drawn from publicly available sources, acquired from third-parties, or synthetically generated or augmented. Publicly available data includes content from the public internet and publicly accessible repositories.","The training data curation process includes cleaning, processing, and modifying datasets. These processing steps, which vary by data type, may include deduplication and filtering to remove junk or other low-quality data, or to advance safety or other objectives.",[459,474,474,474,475,476,477,478,461,479],"# Call the server using curl (OpenAI-compatible API):","Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-following, and other natural language and multimodal tasks. It is released with open weights to support research, fine-tuning and integration into third-party products by downstream developers.","Languages: English, with general multilingual capabilities across other languages.","Try Inkling on the Tinker Playground or access via API using the Tinker Cookbook .","API access is also available through third party inference providers.","Conduct their own evaluation of Inkling's performance, safety, and fairness for their specific use case, language, and population prior to deployment, particularly for applications involving vulnerable groups.","thinkingmachines\u002FInkling · Hugging Face \nHugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up \\\", \\\"assistant\\\": \\\"\u003C|message_model|>\\\", \\\"system\\\": \\\"\u003C|message_system|>\\\", \\\"tool\\\": \\\"\u003C|message_tool|>\\\"} -%}\\n\\n{%- macro emit_thinking_effort() -%}\\n {%- set eff = reasoning_effort if reasoning_effort is defined and reasoning_effort is not none else 0.9 -%}\\n {%- if eff is string -%}\\n {%- set key = eff | trim -%}\\n {%- if key not in effort_map -%}\\n {{- raise_exception(\\\"Unknown reasoning_effort: \\\" ~ eff) -}}\\n {%- endif -%}\\n {%- set num = effort_map[key] -%}\\n {%- else -%}\\n {%- set num = eff | float -%}\\n {%- endif -%}\\n {%- if num \u003C 0.0 or num > 0.99 -%}\\n {{- raise_exception(\\\"reasoning_effort must be in [0.0, 0.99]\\\") -}}\\n {%- endif -%}\\n {{- \\\"\u003C|message_system|>\u003C|content_text|>Thinking effort level: \\\" -}}\\n {%- if num == 0.0 -%}0{%- else -%}{{ num }}{%- endif -%}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n{%- endmacro -%}\\n\\n{%- if tools -%}\\n {%- set tool_state = namespace(specs=[]) -%}\\n {%- for tool in tools -%}\\n {%- set fn = tool.function if tool.function is defined else tool -%}\\n {%- set spec = {\\n \\\"description\\\": (fn.description if fn.description is defined and fn.description else \\\"\\\"),\\n \\\"name\\\": fn.name,\\n \\\"parameters\\\": (fn.parameters if fn.parameters is defined and fn.parameters else {}),\\n \\\"type\\\": (tool.type if tool.type is defined and tool.type else \\\"function\\\"),\\n } -%}\\n {%- set tool_state.specs = tool_state.specs + [spec] -%}\\n {%- endfor -%}\\n {{- \\\"\u003C|message_system|>tool_declare\u003C|content_xml|>\\\" -}}\\n {{- tool_state.specs | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\")) -}}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n{%- endif -%}\\n\\n{%- set state = namespace(effort_emitted=false) -%}\\n{%- for message in messages -%}\\n {%- if message.role not in role_token -%}\\n {{- raise_exception(\\\"Unknown message role: \\\" ~ message.role) -}}\\n {%- endif -%}\\n {%- if not state.effort_emitted and message.role != \\\"system\\\" -%}\\n {{- emit_thinking_effort() -}}\\n {%- set state.effort_emitted = true -%}\\n {%- endif -%}\\n\\n {%- set rtok = role_token[message.role] -%}\\n\\n {%- if message.role == \\\"tool\\\" -%}\\n {%- set tool_name_state = namespace(name=\\\"\\\") -%}\\n {%- if message.name is defined and message.name -%}\\n {%- set tool_name_state.name = message.name -%}\\n {%- elif message.tool_call_id is defined and message.tool_call_id -%}\\n {%- for prev in messages -%}\\n {%- if prev.role == \\\"assistant\\\" and prev.tool_calls -%}\\n {%- for tc in prev.tool_calls -%}\\n {%- if tc.id is defined and tc.id == message.tool_call_id and tc.function.name is defined -%}\\n {%- set tool_name_state.name = tc.function.name -%}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n {{- rtok -}}\\n {%- if tool_name_state.name -%}{{- tool_name_state.name -}}{%- endif -%}\\n {{- \\\"\u003C|content_text|>\\\" -}}\\n {%- if message.content is string -%}{{- message.content -}}{%- endif -%}\\n {{- \\\"\u003C|end_message|>\\\" -}}\\n\\n {%- else -%}\\n {%- if message.role == \\\"assistant\\\" and message.reasoning_content is defined and message.reasoning_content -%}\\n {{- \\\"\u003C|message_model|>\u003C|content_thinking|>\\\" ~ message.reasoning_content ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- endif -%}\\n\\n {%- if message.content is string -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ message.content ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif message.content -%}\\n {%- for part in message.content -%}\\n {%- if part is string -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ part ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif part.type is not defined or part.type in (\\\"text\\\", \\\"input_text\\\") -%}\\n {%- set text_part = (part.text if part.text is defined and part.text is string else \\\"\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_text|>\\\" ~ text_part ~ \\\"\u003C|end_message|>\\\" -}}\\n {%- elif part.type in (\\\"image\\\", \\\"input_image\\\", \\\"image_url\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_image|>\u003C|unused_200054|>\u003C|end_message|>\\\" -}}\\n {%- elif part.type in (\\\"audio\\\", \\\"input_audio\\\", \\\"audio_url\\\") -%}\\n {{- rtok ~ \\\"\u003C|content_audio_input|>\u003C|unused_200053|>\u003C|audio_end|>\u003C|end_message|>\\\" -}}\\n {%- else -%}\\n {{- raise_exception(\\\"Unsupported content part type: \\\" ~ part.type) -}}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endif -%}\\n\\n {%- if message.role == \\\"assistant\\\" and message.tool_calls -%}\\n {%- for tc in message.tool_calls -%}\\n {%- set fn = tc.function -%}\\n {%- if fn.name is not defined or fn.name is not string -%}\\n {{- raise_exception(\\\"tool call function name must be a string\\\") -}}\\n {%- endif -%}\\n {%- set args = fn.arguments if fn.arguments is defined and fn.arguments else {} -%}\\n {%- if args is string -%}\\n {{- raise_exception(\\\"tool call arguments must be a parsed object, not a JSON string; canonicalize upstream\\\") -}}\\n {%- endif -%}\\n {%- if args is not mapping -%}\\n {{- raise_exception(\\\"tool call arguments must be an object\\\") -}}\\n {%- endif -%}\\n {{- \\\"\u003C|message_model|>\\\" ~ fn.name ~ \\\"\u003C|content_invoke_tool_json|>\\\" -}}\\n {{- '{\\\"name\\\":' ~ (fn.name | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\"))) ~ ',\\\"args\\\":' -}}\\n {{- (args | tojson(sort_keys=true, separators=(\\\",\\\", \\\":\\\"))) -}}\\n {{- \\\"}\u003C|end_message|>\\\" -}}\\n {%- endfor -%}\\n {%- endif -%}\\n\\n {%- if message.role == \\\"assistant\\\" -%}\\n {{- \\\"\u003C|content_model_end_sampling|>\\\" -}}\\n {%- endif -%}\\n {%- endif -%}\\n{%- endfor -%}\\n\\n{%- if not state.effort_emitted -%}\\n {{- emit_thinking_effort() -}}\\n{%- endif -%}\\n\\n{%- if add_generation_prompt -%}\\n {{- \\\"\u003C|message_model|>\\\" -}}\\n{%- endif -%}\\n\"},\"createdAt\":\"2026-07-14T13:23:14.000Z\",\"discussionsDisabled\":false,\"discussionsSorting\":\"recently-created\",\"downloads\":77799,\"downloadsAllTime\":77799,\"id\":\"thinkingmachines\u002FInkling\",\"isLikedByUser\":false,\"availableInferenceProviders\":[{\"provider\":\"baseten\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002Finkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":true,\"toolCalling\":true},\"isCheapestPricingOutput\":true,\"isFastestThroughput\":true,\"isModelAuthor\":false,\"tokensPerSecond\":158.72110743627016,\"pricingOutput\":4.05},{\"provider\":\"fireworks-ai\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"accounts\u002Ffireworks\u002Fmodels\u002Finkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":false,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":102.38685589674759},{\"provider\":\"together\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002FInkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":true,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":127.45434889475416,\"pricingOutput\":4.05},{\"provider\":\"deepinfra\",\"modelStatus\":\"live\",\"providerStatus\":\"live\",\"providerId\":\"thinkingmachines\u002FInkling\",\"task\":\"conversational\",\"adapterWeightsPath\":\"model-00001-of-00108.safetensors\",\"features\":{\"structuredOutput\":false,\"toolCalling\":true},\"isCheapestPricingOutput\":false,\"isFastestThroughput\":false,\"isModelAuthor\":false,\"tokensPerSecond\":99.40961150436104,\"pricingOutput\":4.05}],\"showHuggingChatEntry\":true,\"inference\":\"warm\",\"lastModified\":\"2026-07-23T17:27:17.000Z\",\"likes\":1703,\"pipeline_tag\":\"image-text-to-text\",\"library_name\":\"transformers\",\"librariesOther\":[],\"trackDownloads\":true,\"model-index\":null,\"evalResults\":[{\"dataset\":{\"id\":\"SWE-bench\u002FSWE-bench_Verified\",\"isBenchmark\":true,\"task_id\":\"swe_bench_%_resolved\"},\"value\":77.6,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling\",\"name\":\"Model 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\u002F Inkling like 1.7k Follow Thinking Machines Lab 1.65k \nImage-Text-to-Text Transformers Safetensors inkling_mm_model conversational audio-text-to-text Mixture of Experts Eval Results License: apache-2.0 Model card Files Files and versions xet Community 17 Deploy Copy to bucket new Use this model Instructions to use thinkingmachines\u002FInkling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.\nLibraries Transformers How to use thinkingmachines\u002FInkling with Transformers:\n# Use a pipeline as a high-level helper\nfrom transformers import pipeline\npipe = pipeline(\"image-text-to-text\", model=\"thinkingmachines\u002FInkling\")\nmessages = [\n{\n\"role\": \"user\",\n\"content\": [\n{\"type\": \"image\", \"url\": \"https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fhuggingface\u002Fdocumentation-images\u002Fresolve\u002Fmain\u002Fp-blog\u002Fcandy.JPG\"},\n{\"type\": \"text\", \"text\": \"What animal is on the candy?\"}\n]\n},\n]\npipe(text=messages) # Load model directly\nfrom transformers import AutoProcessor, AutoModelForMultimodalLM\nprocessor = AutoProcessor.from_pretrained(\"thinkingmachines\u002FInkling\")\nmodel = AutoModelForMultimodalLM.from_pretrained(\"thinkingmachines\u002FInkling\", device_map=\"auto\")\nmessages = [\n{\n\"role\": \"user\",\n\"content\": [\n{\"type\": \"image\", \"url\": \"https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fhuggingface\u002Fdocumentation-images\u002Fresolve\u002Fmain\u002Fp-blog\u002Fcandy.JPG\"},\n{\"type\": \"text\", \"text\": \"What animal is on the candy?\"}\n]\n},\n]\ninputs = processor.apply_chat_template(\nmessages,\nadd_generation_prompt=True,\ntokenize=True,\nreturn_dict=True,\nreturn_tensors=\"pt\",\n).to(model.device)\noutputs = model.generate(**inputs, max_new_tokens=40)\nprint(processor.decode(outputs[0][inputs[\"input_ids\"].shape[-1]:])) Inference Inference Providers HuggingChat Notebooks Google Colab Kaggle Local Apps Settings vLLM How to use thinkingmachines\u002FInkling with vLLM:\nInstall from pip and serve model\n# Install vLLM from pip:\npip install vllm\n# Start the vLLM server:\nvllm serve \"thinkingmachines\u002FInkling\"\n# Call the server using curl (OpenAI-compatible API):\ncurl -X POST \"http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions\" \\\n-H \"Content-Type: application\u002Fjson\" \\\n--data '{\n\"model\": \"thinkingmachines\u002FInkling\",\n\"messages\": [\n{\n\"role\": \"user\",\n\"content\": [\n{\n\"type\": \"text\",\n\"text\": \"Describe this image in one sentence.\"\n},\n{\n\"type\": \"image_url\",\n\"image_url\": {\n\"url\": \"https:\u002F\u002Fcdn.britannica.com\u002F61\u002F93061-050-99147DCE\u002FStatue-of-Liberty-Island-New-York-Bay.jpg\"\n}\n}\n]\n}\n]\n}' Use Docker\ndocker model run hf.co\u002Fthinkingmachines\u002FInkling SGLang How to use thinkingmachines\u002FInkling with SGLang:\nInstall from pip and serve model\n# Install SGLang from pip:\npip install sglang\n# Start the SGLang server:\npython3 -m sglang.launch_server \\\n--model-path \"thinkingmachines\u002FInkling\" \\\n--host 0.0.0.0 \\\n--port 30000\n# Call the server using curl (OpenAI-compatible API):\ncurl -X POST \"http:\u002F\u002Flocalhost:30000\u002Fv1\u002Fchat\u002Fcompletions\" \\\n-H \"Content-Type: application\u002Fjson\" \\\n--data '{\n\"model\": \"thinkingmachines\u002FInkling\",\n\"messages\": [\n{\n\"role\": \"user\",\n\"content\": [\n{\n\"type\": \"text\",\n\"text\": \"Describe this image in one sentence.\"\n},\n{\n\"type\": \"image_url\",\n\"image_url\": {\n\"url\": \"https:\u002F\u002Fcdn.britannica.com\u002F61\u002F93061-050-99147DCE\u002FStatue-of-Liberty-Island-New-York-Bay.jpg\"\n}\n}\n]\n}\n]\n}' Use Docker images\ndocker run --gpus all \\\n--shm-size 32g \\\n-p 30000:30000 \\\n-v ~\u002F.cache\u002Fhuggingface:\u002Froot\u002F.cache\u002Fhuggingface \\\n--env \"HF_TOKEN=\u003Csecret>\" \\\n--ipc=host \\\nlmsysorg\u002Fsglang:latest \\\npython3 -m sglang.launch_server \\\n--model-path \"thinkingmachines\u002FInkling\" \\\n--host 0.0.0.0 \\\n--port 30000\n# Call the server using curl (OpenAI-compatible API):\ncurl -X POST \"http:\u002F\u002Flocalhost:30000\u002Fv1\u002Fchat\u002Fcompletions\" \\\n-H \"Content-Type: application\u002Fjson\" \\\n--data '{\n\"model\": \"thinkingmachines\u002FInkling\",\n\"messages\": [\n{\n\"role\": \"user\",\n\"content\": [\n{\n\"type\": \"text\",\n\"text\": \"Describe this image in one sentence.\"\n},\n{\n\"type\": \"image_url\",\n\"image_url\": {\n\"url\": \"https:\u002F\u002Fcdn.britannica.com\u002F61\u002F93061-050-99147DCE\u002FStatue-of-Liberty-Island-New-York-Bay.jpg\"\n}\n}\n]\n}\n]\n}' Docker Model Runner How to use thinkingmachines\u002FInkling with Docker Model Runner:\ndocker model run hf.co\u002Fthinkingmachines\u002FInkling Browse\nQuantizations to use this model in llama.cpp , Ollama , LM Studio , or any compatible app. Inkling 1. General Information 2. Getting Started 3. Model Properties Model type Architecture type Parameters Numerics support Input modalities Output modalities 4. Training 5. Evaluations 6. Safety 7. Bias, risks and limitations \nInkling\nBF16 |\nNVFP4 |\nPlayground |\nTinker Cookbook |\nAcceptable Use \n1. General Information\nInkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-following, and other natural language and multimodal tasks. It is released with open weights to support research, fine-tuning and integration into third-party products by downstream developers.\nLanguages: English, with general multilingual capabilities across other languages.\n2. Getting Started\nTry Inkling on the Tinker Playground or access via API using the Tinker Cookbook .\nInkling supports local deployment using the following open-source libraries:\nSGLang ( recipe ) \nvLLM ( recipe ) \nTokenSpeed ( recipe ) \nUnsloth ( recipe ) \nHuggingface ( recipe ) \nAPI access is also available through third party inference providers.\n3. Model Properties\nModel type\nMultimodal autoregressive transformer\nArchitecture type\nA 66-layer decoder-only transformer with a sparse Mixture-of-Experts (MoE) feed-forward backbone: each token is routed to 6 of 256 experts, plus 2 shared experts active on every token. Attention is a hybrid of local and global layers. The model is natively multimodal — images and video are encoded via a hierarchical patch encoder, and audio via discrete token encoding — with all modalities projected into a shared hidden",[482,499,516,561,596,611,643,662],{"url":483,"kind":484,"anchor":485,"title":486,"description":437,"headings":487,"feature_evidence":489,"use_case_evidence":496,"fetched_at":497,"status":425,"content_hash":498},"https:\u002F\u002Fhuggingface.co\u002Fdocs","feature","Docs","Hugging Face - Documentation",[488],"Documentation",[490,491,492,493,494,495],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Documentation","Guides, references, and API docs for the Hugging Face ecosystem.","API for metadata, stats, and content of HF Hub datasets","Serve language models with TGI optimized toolkit","Parameter-efficient finetuning for large language models","AutoTrain API and UI for seamless model training",[490],"2026-08-09T01:07:44.900Z","990104750e302df42cfbe57c0e9bda24a99fe40a002a982fabe2c01f891a38ce",{"url":500,"kind":484,"anchor":352,"title":501,"description":437,"headings":502,"feature_evidence":510,"use_case_evidence":513,"fetched_at":514,"status":425,"content_hash":515},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fsafetensors","Safetensors · Hugging Face",[503,503,504,505,506,507,508,509],"Safetensors","Installation","Usage","Load tensors","Save tensors","Format","Featured Projects",[511,512],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Safetensors documentation","🏡 View all docs AWS Trainium & Inferentia Accelerate Argilla AutoTrain Bitsandbytes CLI Chat UI Dataset viewer Datasets Deploying on AWS Diffusers Distilabel Evaluate Google Cloud Google TPUs Gradio Hub Hub Python Library Huggingface.js Inference Endpoints (dedicated) Inference Providers Kernels LeRobot Leaderboards Lighteval Microsoft Azure OpenEnv Optimum PEFT Reachy Mini Safetensors Sentence Transformers TRL Tasks Text Embeddings Inference Text Generation Inference Tokenizers Trackio Transformers Transformers.js Xet smolagents timm Search documentation main v0.5.0-rc.0 v0.3.2 v0.2.9 EN Getting started 🤗 Safetensors Speed Comparison Tensor Sharing in Pytorch Metadata Parsing Convert weights to safetensors API Torch API Tensorflow API PaddlePaddle API Flax API Numpy API You are viewing main version, which requires installation from source . If you'd like",[511],"2026-08-09T01:07:45.451Z","c698daac80d92308eba5b562cc1bf12e1cba86f6ef0b3e5bad9251fbae968243",{"url":517,"kind":484,"anchor":518,"title":519,"description":437,"headings":520,"feature_evidence":538,"use_case_evidence":557,"fetched_at":559,"status":425,"content_hash":560},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Finference-providers","NEW","Inference Providers · Hugging Face",[521,521,522,523,524,525,526,527,528,529,530,531,532,533,530,531,534,535,536,537],"Inference Providers","Quick Setup for Agents","Partners","Why Choose Inference Providers?","Key Features","Getting Started","Inference Playground","Authentication","Quick Start - LLM","Python","JavaScript","HTTP \u002F cURL","Quick Start - Text-to-Image Generation","Provider Selection","API as a Proxy Service","Client-Side Provider Selection (Inference Clients)","Alternative: OpenAI-Compatible Chat Completions Endpoint (Chat Only)",[539,540,541,542,543,544,545,546,547,548,549,550,551,535,552,553,554,555,556],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Inference Providers documentation","🏡 View all docs AWS Trainium & Inferentia Accelerate Argilla AutoTrain Bitsandbytes CLI Chat UI Dataset viewer Datasets Deploying on AWS Diffusers Distilabel Evaluate Google Cloud Google TPUs Gradio Hub Hub Python Library Huggingface.js Inference Endpoints (dedicated) Inference Providers Kernels LeRobot Leaderboards Lighteval Microsoft Azure OpenEnv Optimum PEFT Reachy Mini Safetensors Sentence Transformers TRL Tasks Text Embeddings Inference Text Generation Inference Tokenizers Trackio Transformers Transformers.js Xet smolagents timm Search documentation main EN Get Started Inference Providers Pricing and Billing Hub integration Security Guides Your First API Call Building Your First AI App Structured Outputs with LLMs Function Calling Responses API (beta) How to use OpenAI gpt-oss Build an Image Editor Automating Code Review with GitHub Actions Agentic Coding Environments with OpenEnv Evaluating Models with Inspect Integrations Overview Pi OpenCode Codex Claude Code Hermes Agent NeMo Data Designer MacWhisper Vision Agents VS Code with GitHub Copilot Add Your Integration Inference Tasks Chat Completion Feature Extraction Text to Image Text to Video Other Tasks Providers Baseten Cerebras Cohere DeepInfra Fal AI Featherless AI Fireworks Groq HF Inference Novita Nscale OVHcloud AI Endpoints Public AI Replicate Scaleway Together WaveSpeedAI Z.ai Hub API Register as an Inference Provider Join the Hugging Face community and get access to the augmented documentation experience","Our platform integrates with leading AI infrastructure providers, giving you access to their specialized capabilities through a single, consistent API. Here’s what each partner supports:","When you build AI applications, it’s tough to manage multiple provider APIs, comparing model performance, and dealing with varying reliability. Inference Providers solves these challenges by offering:","Instant Access to Cutting-Edge Models : Go beyond mainstream providers to access thousands of specialized models across multiple AI tasks. Whether you need the latest language models, state-of-the-art image generators, or domain-specific embeddings, you’ll find them here.","Text Generation : Use Large language models with tool-calling capabilities for chatbots, content generation, and code assistance Image and Video Generation : Create custom images and videos, including support for LoRAs and style customization Search & Retrieval : State-of-the-art embeddings for semantic search, RAG systems, and recommendation engines Traditional ML Tasks : Ready-to-use models for classification, NER, summarization, and speech recognition ⚡ Get Started for Free : Inference Providers includes a generous free tier, with additional credits for PRO users and Team & Enterprise organizations .","🎯 All-in-One API : A single API for text generation, image generation, document embeddings, NER, summarization, image classification, and more. 🔀 Multi-Provider Support : Easily run models from top-tier providers like fal, Replicate, Together AI, and others. 🚀 Scalable & Reliable : Built for high availability and low-latency performance in production environments. 🔧 Developer-Friendly : Simple requests, fast responses, and a consistent developer experience across Python and JavaScript clients. 👷 Easy to integrate : Drop-in replacement for the OpenAI chat completions API. 💰 Cost-Effective : No extra markup on provider rates. Getting Started","Inference Providers works with your existing development workflow. Whether you prefer Python, JavaScript, or direct HTTP calls, we provide native SDKs and OpenAI-compatible APIs to get you up and running quickly.","Prefer the terminal? Run hf models ls --warm with the hf CLI to list every model served by at least one provider, and add --json for scripts and agents. See the Hub API page for the full set of filters.","Let’s start with the most common use case: conversational AI using large language models. This section demonstrates how to perform chat completions using DeepSeek V3, showcasing the different ways you can integrate Inference Providers into your applications.","For testing, debugging, or integrating with any HTTP client, here’s the raw REST API format.","client = InferenceClient(api_key=os.environ[ \"HF_TOKEN\" ])","The Inference Providers API acts as a unified proxy layer that sits between your application and multiple AI providers. Understanding how provider selection works is crucial for optimizing performance, cost, and reliability in your applications.","Unified Authentication & Billing : Use a single Hugging Face token for all providers Automatic Failover : When using automatic provider selection ( provider=\"auto\" ), requests are automatically routed to alternative providers if the primary provider is flagged as unavailable by our validation system Consistent Interface through client libraries : When using our client libraries, the same request format works across different providers Because the API acts as a proxy, the exact HTTP request may vary between providers as each provider has their own API requirements and response formats. When using our official client libraries (JavaScript or Python), these provider-specific differences are handled automatically whether you use provider=\"auto\" or specify a particular provider.","When using the Hugging Face inference clients (JavaScript or Python), you can explicitly specify a provider or let the system choose automatically. The client then formats the HTTP request to match the selected provider’s API requirements.","If you prefer to work with familiar OpenAI APIs or want to migrate existing chat completion code with minimal changes, we offer a drop-in compatible endpoint that handles all provider selection automatically on the server side.","apiKey : process. env . HF_TOKEN ,","Announcement Blog Post : Learn more about the launch of Inference Providers Pricing and Billing : Understand costs and billing of Inference Providers Hub Integration : Learn how Inference Providers are integrated with the Hugging Face Hub Register as a Provider : Requirements to join our partner network as a provider Hub API : Advanced API features and configuration API Reference : Complete parameter documentation for all supported tasks Update on GitHub",[539,546,558,548],"Before diving into integration, explore models interactively with our Inference Playground . Test different chat completion models with your prompts and compare responses to find the perfect fit for your use case.","2026-08-09T01:07:45.885Z","b28dbeccbfc963ee5708fd8619a1a264fbe4e0b52183939a94677f56a883261c",{"url":562,"kind":484,"anchor":352,"title":563,"description":437,"headings":564,"feature_evidence":585,"use_case_evidence":593,"fetched_at":594,"status":425,"content_hash":595},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fmodel-cards#specifying-a-base-model","Model Cards · Hugging Face",[565,566,567,568,569,570,571,572,573,574,575,576,577,578,579,580,581,582,583,584],"Hub","Model Cards","What are Model Cards?","Model card metadata","Adding metadata to your model card","Using the metadata UI","Editing the YAML section of the README.md file","Specifying a library","Specifying a base model","Specifying a new version","Specifying a dataset","Specifying a bucket","Specifying a task ( pipeline_tag )","Specifying a license","Evaluation Results","CO2 Emissions","Linking a Paper","Model Card text","FAQ","How are model tags determined?",[586,587,588,589,590,591,592],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Hub documentation","🏡 View all docs AWS Trainium & Inferentia Accelerate Argilla AutoTrain Bitsandbytes CLI Chat UI Dataset viewer Datasets Deploying on AWS Diffusers Distilabel Evaluate Google Cloud Google TPUs Gradio Hub Hub Python Library Huggingface.js Inference Endpoints (dedicated) Inference Providers Kernels LeRobot Leaderboards Lighteval Microsoft Azure OpenEnv Optimum PEFT Reachy Mini Safetensors Sentence Transformers TRL Tasks Text Embeddings Inference Text Generation Inference Tokenizers Trackio Transformers Transformers.js Xet smolagents timm Search documentation EN API docs 🤗 Hugging Face Hub Team & Enterprise Plans Single Sign-On (SSO) Audit Logs Storage Regions Data Studio for Private datasets Resource Groups (Access Control) Advanced Compute Options Advanced Security Tokens Management Service Accounts Publisher Analytics Gating Group Collections Network Security Download Analytics Rate Limits Blog Articles PRO Plan Repositories Getting Started with Repositories Repository Settings Storage Limits Storage Backend (Xet) Local Cache Pull Requests & Discussions Notifications Collections Webhooks GitHub Actions Notebooks Next Steps Licenses Models The Model Hub Model Cards Carbon Emissions Card Components Eval Results Leaderboard Data Gated Models Uploading Models Downloading Models Integrated Libraries Model Widgets Model Inference Models Download Stats Model Release Checklist Hardware Local Apps Frequently Asked Questions Advanced Topics Datasets Datasets Overview Dataset Cards Gated Datasets Uploading Datasets Uploading Datasets (for LLMs) Downloading Datasets Streaming Datasets Integrated Libraries Data Studio Agent Traces Datasets Download Stats Spaces Spaces Overview Spaces GPU Upgrades Spaces ZeroGPU Spaces Dev Mode Spaces Disk Usage & Storage Spaces Custom Domain Spaces as MCP servers Spaces as Agent Tools Spaces as API Endpoints Gradio Spaces Streamlit Spaces Static HTML Spaces Docker Spaces Embed your Space Run Spaces with Docker Spaces Configuration Reference Sign-In with HF button Featured Spaces Spaces Changelog Advanced Topics Storage Buckets new Access Patterns S3 Compatibility Bucket Integrations Bucket Security Jobs Jobs Overview Quickstart Pricing and Billing Manage Jobs Configuration Popular Images Serve Models Examples & Tutorials Process Large Datasets Schedule Jobs Webhook Automation Reference Agents Agents Overview Hugging Face CLI for AI Agents Hugging Face MCP Server Hugging Face Agent Skills Building agents with the HF SDK Local Agents with llama.cpp Agent Libraries Session Traces Format Other Organizations Billing Security Moderation Paper Pages Academia Hub Blog Articles Search Digital Object Identifier (DOI) Hub API Endpoints OAuth \u002F Sign in with HF Join the Hugging Face community and get access to the augmented documentation experience","the model its intended uses & potential limitations, including biases and ethical considerations as detailed in Mitchell, 2018 the training params and experimental info (you can embed or link to an experiment tracking platform for reference) which datasets were used to train your model the model’s evaluation results The model card template is available here .","Allowing users to filter models at https:\u002F\u002Fhuggingface.co\u002Fmodels . Displaying the model’s license. Adding datasets to the metadata will add a message reading Datasets used to train: to your model page and link the relevant datasets, if they’re available on the Hub. Dataset and language identifiers are those listed on the Datasets and Languages pages.","language:","- \"List of ISO 639-1 code for your language\"","You can specify the pipeline_tag in the model card metadata. The pipeline_tag indicates the type of task the model is intended for. This tag will be displayed on the model page and users can filter models on the Hub by task. This tag is also used to determine which widget to use for the model and which APIs to use under the hood.",[586],"2026-08-09T01:07:46.209Z","4a2649199209784210d6ea49562458af1f67c515f1dc756887ad90e4fc72a4c6",{"url":597,"kind":484,"anchor":352,"title":598,"description":437,"headings":599,"feature_evidence":606,"use_case_evidence":608,"fetched_at":609,"status":425,"content_hash":610},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Feval-results","Evaluation Results · Hugging Face",[565,579,600,601,602,603,604,605],"Benchmark Datasets","Model Evaluation Results","Adding Evaluation Results","Community Contributions","Registering a Benchmark","Eval.yaml specification",[586,607],"🏡 View all docs AWS Trainium & Inferentia Accelerate Argilla AutoTrain Bitsandbytes CLI Chat UI Dataset viewer Datasets Deploying on AWS Diffusers Distilabel Evaluate Google Cloud Google TPUs Gradio Hub Hub Python Library Huggingface.js Inference Endpoints (dedicated) Inference Providers Kernels LeRobot Leaderboards Lighteval Microsoft Azure OpenEnv Optimum PEFT Reachy Mini Safetensors Sentence Transformers TRL Tasks Text Embeddings Inference Text Generation Inference Tokenizers Trackio Transformers Transformers.js Xet smolagents timm Search documentation EN API docs 🤗 Hugging Face Hub Team & Enterprise Plans Single Sign-On (SSO) Audit Logs Storage Regions Data Studio for Private datasets Resource Groups (Access Control) Advanced Compute Options Advanced Security Tokens Management Service Accounts Publisher Analytics Gating Group Collections Network Security Download Analytics Rate Limits Blog Articles PRO Plan Repositories Getting Started with Repositories Repository Settings Storage Limits Storage Backend (Xet) Local Cache Pull Requests & Discussions Notifications Collections Webhooks GitHub Actions Notebooks Next Steps Licenses Models The Model Hub Model Cards Eval Results Leaderboard Data Gated Models Uploading Models Downloading Models Integrated Libraries Model Widgets Model Inference Models Download Stats Model Release Checklist Hardware Local Apps Frequently Asked Questions Advanced Topics Datasets Datasets Overview Dataset Cards Gated Datasets Uploading Datasets Uploading Datasets (for LLMs) Downloading Datasets Streaming Datasets Integrated Libraries Data Studio Agent Traces Datasets Download Stats Spaces Spaces Overview Spaces GPU Upgrades Spaces ZeroGPU Spaces Dev Mode Spaces Disk Usage & Storage Spaces Custom Domain Spaces as MCP servers Spaces as Agent Tools Spaces as API Endpoints Gradio Spaces Streamlit Spaces Static HTML Spaces Docker Spaces Embed your Space Run Spaces with Docker Spaces Configuration Reference Sign-In with HF button Featured Spaces Spaces Changelog Advanced Topics Storage Buckets new Access Patterns S3 Compatibility Bucket Integrations Bucket Security Jobs Jobs Overview Quickstart Pricing and Billing Manage Jobs Configuration Popular Images Serve Models Examples & Tutorials Process Large Datasets Schedule Jobs Webhook Automation Reference Agents Agents Overview Hugging Face CLI for AI Agents Hugging Face MCP Server Hugging Face Agent Skills Building agents with the HF SDK Local Agents with llama.cpp Agent Libraries Session Traces Format Other Organizations Billing Security Moderation Paper Pages Academia Hub Blog Articles Search Digital Object Identifier (DOI) Hub API Endpoints OAuth \u002F Sign in with HF Join the Hugging Face community and get access to the augmented documentation experience",[586],"2026-08-09T01:07:46.640Z","56903532887cc529d57f773b5b03a03571e7b8d2432993ef10249939d1502d20",{"url":612,"kind":613,"anchor":614,"title":615,"description":616,"headings":617,"feature_evidence":637,"use_case_evidence":640,"fetched_at":641,"status":425,"content_hash":642},"https:\u002F\u002Fhuggingface.co\u002Fpapers","research","Daily Papers","Daily Papers - Hugging Face","Your daily dose of AI research from AK",[614,618,619,620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636],"by AK and the research community","AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models","Interpretable MEG Decoding of Perceived Speech: Cortical Sources and the Stimulus Features That Drive Retrieval","WorldClaw: Agentic 3D Open-World Generation at Scale","GST-Bench: Can VLMs Develop Global Spatial Awareness from Video?","EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning","ChronoVision: Temporal Reasoning via Latent State Reconstruction","Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval","HarnessOpt-Bench: Evaluating LLMs at Harness Optimization","From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models","DataSpace: Benchmarking Data Agents for Verifiable Analytics over Heterogeneous Workspaces","On-Policy Delta Distillation for Multilingual Math Reasoning","Teaching Nemotron Greek: Mining a Corpus, Adapting Retrieval, and Grounding Generation for Modern Greek across Specialist Domains","World-to-Wrist: Task-Conditioned Future Wrist Modeling for Fine-Grained Robot Manipulation","DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation","Activity Frames: Deterministic Screen-Activity Compilation for Agent Memory and Replay","SmartMage: Dynamic Modality Orchestration for 3D Scene Understanding","EffectLearner: World-Aware Object-Effect Reasoning for Real-World Video Object Removal",[638,639],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up new Get trending papers in your email inbox once a day!","Kandinsky Lab 53 3 Submitted by Uri-ka 14 MameLoshnLM: Yiddish Language Model and Evaluation Benchmark",[638],"2026-08-09T01:07:47.106Z","81ef1f8e47d94fcea90328e6f9de0dfd619225623f7b00abb0d70324368cf872",{"url":644,"kind":645,"title":644,"description":352,"feature_evidence":646,"use_case_evidence":659,"fetched_at":660,"status":425,"content_hash":661},"https:\u002F\u002Fhuggingface.co\u002Fsitemap-blog.xml","blog",[647,648,649,650,651,652,653,654,655,656,657,658],"https:\u002F\u002Fhuggingface.co\u002Fblog\u002Frapidfireai","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fanylanguagemodel","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fintel-protein-language-model-protst","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Ftgi-messages-api","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Frun-musicgen-as-an-api","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Funity-api","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fchinese-language-blog","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fintel-sapphire-rapids-inference","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fvision_language_pretraining","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fintel-sapphire-rapids","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Flarge-language-models","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Ffew-shot-learning-gpt-neo-and-inference-api",[],"2026-08-09T01:07:47.450Z","6f233b122ad09e75c57f6654c77242f0f50f9b12865961ac10367d14d836bb82",{"url":663,"kind":613,"title":663,"description":352,"feature_evidence":664,"use_case_evidence":665,"fetched_at":666,"status":425,"content_hash":667},"https:\u002F\u002Fhuggingface.co\u002Fsitemap-papers.xml",[],[],"2026-08-09T01:07:48.311Z","ad2833e02848abff8ab6f61e4988203adb61395a39ac87b4aad5879cda9bddf4",[669,670],{"url":612,"title":614,"kind":613},{"url":663,"title":663,"kind":613},[672],{"title":644,"url":644,"fetched_at":660},[],[459,474,474,474,475,476,477,478,461,479,490,491,492,493,494,495,511,512,539,540,541,542,543,544,545,546,547,548,549,550,551,535,552,553,554,555,556,586,587,588,589,590,591,592,586,607],"yes",[677],{"fetched_at":678,"title":679,"url":372},"2026-07-23T17:27:17.000Z","Introducing Inkling: A New Era of Open-Weights AI","unknown","Tak, model był pretrenowany na 45 bilionach tokenów tekstu, obrazów, audio i wideo z danych publicznie dostępnych, od stron trzecich oraz syntetycznie generowanych\u002Faugmentowanych.","model",[684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700,701],"Natywna multimodalność: tekst, obraz i audio jako wejście, tekst jako wyjście.","Okno kontekstu do 1M tokenów.","Kontrolowany 'thinking effort' pozwalający balansować jakość i koszt tokenów.","Architektura Mixture-of-Experts z 975B parametrów całkowitych i 41B aktywnych.","Wsparcie dla fine-tuningu przez Tinker.","Dostęp przez API partnerów inference oraz przez Hugging Face weights.","Wsparcie lokalnego wdrożenia w SGLang, vLLM, TokenSpeed, Unsloth i Hugging Face Transformers.","Narzędzia do pracy z audio i obrazami, w tym cookbook i playground.","multimodal input (text, images, audio)","output in text and code","context window of up to 1 million tokens","adjustable reasoning effort (fine-tuning of thinking depth)","Mixture of Experts (MoE) architecture","support for tool calling","structured output","available in multiple quantization formats (GGUF, BF16, NVFP4)","fine-tuning available via Tinker","open weights under Apache 2.0 license","Yes, the model weights are freely downloadable on Hugging Face under the Apache 2.0 license.",[704,705,706,707,708,709,710,711,712,713,714,715,367,716,717,718],"Tinker Playground","Tinker Cookbook","TogetherAI","Fireworks","Modal","Databricks","Baseten","SGLang","vLLM","TokenSpeed","Unsloth","Hugging Face Transformers","Tinker","Together AI","Fireworks AI",[720,721],"English","other languages","Apache 2.0",[724,725,726,727,728,729,730,731],"Wydajność może być nierówna między językami, dialektami i domenami słabiej reprezentowanymi w treningu.","Ograniczona wiedza do momentu cutoff treningu.","Może generować nieprawdziwe lub niepodparte odpowiedzi.","Producent zaleca dodatkowe zabezpieczenia i nadzór w zastosowaniach wysokiego ryzyka.","Outputs are limited to text and code; no image generation","Performance is lower than top-tier proprietary models","Full model requires large GPU clusters","Only available in specific quantized formats for local inference",[305,733],"Inkling-Small","Nie wykazano",[736],{"plan_name":737,"access_model":738,"price":739,"currency":740,"billing_period":741,"included_usage":680,"trial_days":680,"card_required":742,"source_evidence":433},"Inference via API partners","open_weights","4.05","USD","per_token","no","Brak zweryfikowanych oficjalnych cen startowych i planów cenowych w zebranym materiale. Producent podaje jedynie, że Inkling jest dostępny na Tinker z 50% zniżką przez ograniczony czas, a pełne ceny są w dokumentacji.","Nie znaleziono potwierdzenia, aby Inkling był trenowany na danych użytkowników. Zebrany materiał opisuje trening na danych publicznych, od stron trzecich oraz syntetycznych, ale nie podaje osobnej polityki prywatności ani regionu przechowywania danych dla samego modelu.","2026-07-15",[],"Inkling to pierwszy open-weights model Thinking Machines Lab, ogłoszony 15 lipca 2026. Jest to multimodalny MoE z 975B parametrami totalnymi, 41B aktywnymi, kontekstem do 1M tokenów i natywną obsługą tekstu, obrazu i audio. Producent pozycjonuje go jako bazę do customizacji, fine-tuningu i wdrożeń przez partnerów API oraz self-hosting.","Producent informuje o wcześniejszych ewaluacjach bezpieczeństwa dla zwykłej interakcji człowiek-AI i testów dangerous-capability (CBRN, cyber, loss of control). Wykryte ryzyka resztkowe obejmują m.in. sporadyczną skłonność do odpowiadania na role-play i pośrednio sformułowane szkodliwe prompty; zalecana jest obrona warstwowa po stronie wdrożenia.","Inkling is a 975B-parameter open-weights multimodal AI model from Thinking Machines Lab that supports text, image, and audio input, offers a 1M token context window, and can be fine-tuned via Tinker or used through API partners.",[751,752,753,367,716,754,755,756],"Web","self-hosted Linux\u002FGPU environments","partner inference platforms","llama.cpp","Ollama","LM Studio",[758,759,760,761,762,763,764],"developers","researchers","ML engineers","teams building agentic systems","organizations customizing foundation models","enterprise teams","AI engineers",[766,767,768,769,770,771,772,773,774,613,775,776,777,778,779,780],"agentic coding","tool use","chatbots","retrieval-augmented generation","instruction following","general conversational use","audio transcription and question answering","vision reasoning","fine-tuning\u002Fcustomization for domain-specific workflows","content generation","SEO","automation","transcription","code generation","infrastructure","Thinking Machines Lab","published","2026-08-12T18:20:31.057Z",[733,785,786,787,788],"DeepSeek V3","Nemotron 3 Ultra","GLM 5.2","Kimi K2.5",{"checked_at":783,"ready":429,"failed":790},[],"Nieznana kategoria: infrastruktura\u002Fapi-modele (auto-fix)","```markdown\n# **Inkling – multimodalny model AI do przetwarzania tekstu, obrazów i audio**\n\n**Inkling** to zaawansowany, otwarty model AI o architekturze *Mixture-of-Experts* (MoE) z **975 miliardami parametrów**, opracowany przez *Thinking Machines Lab*. Narzędzie wyróżnia się **natywną multimodalnością** – przetwarza tekst, obrazy i audio (wyjściem jest tekst), a także oferuje **okno kontekstu do 1 mln tokenów**. Jest skierowane do deweloperów, badaczy i firm poszukujących elastycznego, wysokowydajnego modelu do zadań takich jak *agentic coding*, obsługa narzędzi czy tworzenie zaawansowanych chatbotów.\n\n---\n\n## **Najważniejsze funkcje**\n\n1. **Multimodalność w jednym modelu**\n   Inkling akceptuje **tekst, obrazy i audio** jako dane wejściowe, generując tekstowe odpowiedzi. Dzięki temu sprawdza się w zadaniach wymagających analizy wielu typów danych jednocześnie – np. transkrypcja mowy z kontekstem wizualnym czy generowanie opisu obrazu z uwzględnieniem pytań użytkownika.\n\n2. **Ogromne okno kontekstu (1M tokenów)**\n   Model radzi sobie z **długimi dokumentami, kodem źródłowym czy rozmowami**, zachowując spójność nawet przy bardzo rozbudowanych wejściach. To kluczowe dla zastosowań takich jak analiza kodu czy tworzenie agentów AI z pamięcią kontekstową.\n\n3. **Kontrola nad \"wysiłkiem myślowym\" (*thinking effort*)**\n   Użytkownik może **balansować między jakością odpowiedzi a kosztem obliczeniowym**, dostosowując poziom \"zaangażowania\" modelu. Przydatne w scenariuszach, gdzie liczy się szybkość (np. chatboty) lub precyzja (np. analiza danych).\n\n4. **Architektura *Mixture-of-Experts* (MoE)**\n   Inkling wykorzystuje **41 miliardów aktywnych parametrów** (z puli 975 mld), co optymalizuje zużycie zasobów przy zachowaniu wysokiej wydajności. Model dynamicznie aktywuje specjalistyczne podsieci (*experts*) w zależności od zadania.\n\n5. **Fine-tuning i integracje**\n   - Możliwość **dostrajania modelu** za pomocą platformy **Tinker** (z 50% zniżką w okresie promocyjnym).\n   - Dostęp przez **API partnerów** lub pobranie wag modelu z **Hugging Face** (licencja Apache 2.0).\n\n---\n\n## **Dla kogo jest Inkling?**\n\n- **Deweloperzy i inżynierowie AI**\n  Idealny do budowy zaawansowanych agentów, narzędzi do analizy kodu (*agentic coding*) czy systemów automatyzujących workflow z użyciem wielu modalności.\n\n- **Badacze i naukowcy**\n  Otwarty charakter modelu (wagi dostępne na Hugging Face) umożliwia eksperymenty z fine-tuningiem, benchmarkingiem czy badaniami nad multimodalnością.\n\n- **Firmy tworzące chatboty i asystentów głosowych**\n  Dzięki obsłudze audio i tekstu, Inkling może być używany do budowy **multimodalnych asystentów** (np. przetwarzanie pytań głosowych z jednoczesną analizą przesłanych obrazów).\n\n- **Startupy i zespoły R&D**\n  Przystępność (darmowe wagi modelu) i skalowalność (MoE) czynią go atrakcyjnym dla projektów wymagających wysokiej wydajności przy ograniczonych budżetach.\n\n- **Twórcy narzędzi do analizy danych**\n  Przydatny w scenariuszach, gdzie konieczne jest łączenie informacji z różnych źródeł (np. generowanie raportów na podstawie obrazów, tekstu i nagrań).\n\n---\n\n## **Cena**\n\n- **Darmowy plan**:\n  Wagi modelu są **dostępne bezpłatnie** na [Hugging Face](https:\u002F\u002Fhuggingface.co\u002F) pod licencją **Apache 2.0**, co pozwala na lokalne uruchomienie i modyfikacje.\n\n- **Płatne opcje**:\n  - **Fine-tuning na platformie Tinker**: Obecnie oferowany z **50% zniżką** (ceny docelowe nie są jawnie podane – szczegóły mają znaleźć się w dokumentacji).\n  - **API partnerów**: Koszty zależą od dostawcy (brak oficjalnych stawek od Thinking Machines Lab).\n\n*Uwaga*: Producent nie publikuje szczegółowych planów cenowych, co może utrudniać oszacowanie kosztów dla firm planujących komercyjne wdrożenia.\n\n---\n\n## **Zalety i wady**\n\n✅ **Zalety**:\n1. **Otwartość i elastyczność**:\n   Darmowe wagi modelu i licencja Apache 2.0 umożliwiają swobodne eksperymenty i wdrożenia lokalne bez ograniczeń licencyjnych.\n\n2. **Multimodalność w praktyce**:\n   Rzadko spotykana kombinacja obsługi tekstu, obrazów i audio w jednym modelu otwiera nowe możliwości dla aplikacji (np. asystenci rozumiejący kontekst wizualny i głosowy).\n\n3. **Skalowalność dzięki MoE**:\n   Architektura *Mixture-of-Experts* pozwala na efektywne wykorzystanie zasobów, aktywując tylko niezbędne części modelu.\n\n❌ **Wady**:\n1. **Brak transparentnych cen**:\n   Niejasne koszty fine-tuningu i API mogą zniechęcać firmy planujące komercyjne użycie.\n\n2. **Wymagania sprzętowe**:\n   Uruchomienie modelu o 975B parametrach lokalnie wymaga **wysokowydajnej infrastruktury** (np. GPU z dużą pamięcią), co może być barierą dla mniejszych zespołów.\n\n3. **Ograniczone wsparcie dla początkujących**:\n   Brak gotowych, przyjaznych",{"source_page_url":304,"final_url":304,"fetched_at":424},{"alternatives_count":184,"active_deals":184},[],[],{"data":798,"_meta":1092,"alternatives":1093,"deals":1094},{"source":302,"source_key":303,"source_page_url":799,"name":800,"slug":801,"description":802,"title":803,"category":309,"access_model":804,"pricing_evidence":805,"capability_evidence":807,"headings":809,"breadcrumbs":810,"pros":811,"cons":822,"external_links":826,"affiliate_links":861,"official_link_candidates":862,"text_excerpt":873,"fetched_at":874,"final_url":799,"http_status":425,"content_type":426,"content_hash":875,"cache_file":876,"published":429,"verification_status":430,"official_url":840,"official_evidence":877,"api_available":1001,"blog_titles":1002,"data_region":1003,"data_training":1004,"entity_type":682,"external_rating":680,"features":1005,"free_summary":680,"gdpr":1019,"integrations":1020,"languages":1028,"license":680,"limitations":1031,"model_names":1037,"polish_support":734,"pricing_plans":1039,"pricing_summary":1043,"privacy_summary":1044,"release_date":1045,"research_links":1046,"research_summary":1049,"security_summary":1050,"self_hosted":1051,"short_description":1052,"starting_price":680,"supported_platforms":1053,"target_users":1057,"trial_summary":680,"use_cases":1067,"vendor":1081,"status":782,"last_verified_at":1082,"alternatives":1083,"quality":1087,"category_v3":265,"category_v3_main":259,"category_v3_label":266,"category_v3_reason":1089,"deep_card":1090,"deep_card_generated":429,"category_v3_main_label":260,"_category_fixed":429,"evidence":1091},"https:\u002F\u002Fwww.aixploria.com\u002Fgemini-3-1-pro\u002F","Gemini 3.1 Pro","gemini-3-1-pro","Gemini 3.1 Pro est un modèle IA de Google capable de gérer des tâches complexes avec du texte, des images, de l'audio ou de la vidéo. Il peut résumer de longues sources, analyser des données et aider à coder des interfaces complètes","Gemini 3.1 Pro : Avis, Prix, Info & 29 IA Alternatives | 2026 | AIxploria","paid",[806],"Freemium Kimi K3",[808,322,323,325],"Doc API",[],[],[812,813,814,815,816,817,818,819,820,821],"bardzo duży kontekst 1M tokenów","obsługa wielu modalności, w tym audio i wideo","silne rozumowanie i kodowanie","dostęp przez kilka oficjalnych kanałów Google","dobre dopasowanie do zadań agentowych i długiego kontekstu","Highly capable for complex, multimodal tasks","Superior performance in reasoning, coding, and long-context understanding","Available via API and multiple platforms (Gemini App, Google AI Studio, Vertex AI, etc.)","Improved safety and tone evaluation results compared to Gemini 3 Pro","Supports agentic workflows and tool use",[823,824,825],"Brak oficjalnie potwierdzonej informacji o self-hostingu; model jest dystrybuowany przez usługi Google","Brak jednoznacznie potwierdzonej publicznie ceny startowej w zebranych źródłach oficjalnych","Szczegóły ograniczeń i polityk bezpieczeństwa są częściowo delegowane do karty modelu Gemini 3 Pro",[827,829,831,833,835,837,839,842,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860],{"url":828,"text":352},"https:\u002F\u002Ftwitter.com\u002Fintent\u002Ftweet?url=https%3A%2F%2Fwww.aixploria.com%2Fgemini-3-1-pro%2F&text=Gemini+3.1+Pro%3A+Un+mod%C3%A8le+IA+de+Google+capable+de+g%C3%A9rer+des+t%C3%A2ches+complexes+avec+du+texte%2C+des+images%2C+de+l%27audio+ou+de+la+vid%C3%A9o.+Il+peut+r%C3%A9sumer+de+longues+sources%2C+analyser+des+donn%C3%A9es+et+aider+%C3%A0+coder+des+interfaces+compl%C3%A8tes",{"url":830,"text":352},"https:\u002F\u002Fwww.facebook.com\u002Fsharer\u002Fsharer.php?u=https%3A%2F%2Fwww.aixploria.com%2Fgemini-3-1-pro%2F",{"url":832,"text":352},"https:\u002F\u002Fwww.linkedin.com\u002FshareArticle?url=https%3A%2F%2Fwww.aixploria.com%2Fgemini-3-1-pro%2F&title=Gemini+3.1+Pro",{"url":834,"text":352},"https:\u002F\u002Ft.me\u002Fshare\u002Furl?url=https%3A%2F%2Fwww.aixploria.com%2Fgemini-3-1-pro%2F&text=Un+mod%C3%A8le+IA+de+Google+capable+de+g%C3%A9rer+des+t%C3%A2ches+complexes+avec+du+texte%2C+des+images%2C+de+l%27audio+ou+de+la+vid%C3%A9o.+Il+peut+r%C3%A9sumer+de+longues+sources%2C+analyser+des+donn%C3%A9es+et+aider+%C3%A0+coder+des+interfaces+compl%C3%A8tes",{"url":836,"text":361},"https:\u002F\u002Fchatgpt.com\u002F?q=Donne-moi%20une%20pr%C3%A9sentation%20claire%20et%20concise%20de%20Gemini%203.1%20Pro.%20Explique%20%C3%A0%20quoi%20sert%20cet%20outil%20IA%2C%20%C3%A0%20qui%20il%20s%E2%80%99adresse%2C%20ses%20principales%20fonctionnalit%C3%A9s%2C%20son%20mod%C3%A8le%20tarifaire%2C%20ses%20points%20forts%2C%20ses%20limites%20et%20ses%20alternatives%20possibles.",{"url":838,"text":364},"https:\u002F\u002Fwww.perplexity.ai\u002F?q=Recherche%20des%20informations%20%C3%A0%20jour%20sur%20Gemini%203.1%20Pro.%20R%C3%A9sume%20%C3%A0%20quoi%20sert%20cet%20outil%2C%20ses%20prix%20actuels%20si%20disponibles%2C%20ses%20principales%20fonctionnalit%C3%A9s%2C%20ses%20points%20forts%2C%20ses%20limites%20et%20ses%20alternatives%20notables.",{"url":840,"text":841},"https:\u002F\u002Fdeepmind.google\u002Fmodels\u002Fmodel-cards\u002Fgemini-3-1-pro\u002F","Carte modèle",{"url":843,"text":808},"https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002Fmodels\u002Fgemini-3.1-pro-preview",{"url":378,"text":379},{"url":378,"text":381},{"url":378,"text":383},{"url":385,"text":386},{"url":385,"text":388},{"url":385,"text":383},{"url":391,"text":392},{"url":391,"text":322},{"url":391,"text":383},{"url":396,"text":397},{"url":396,"text":399},{"url":396,"text":383},{"url":402,"text":352},{"url":404,"text":352},{"url":406,"text":352},{"url":408,"text":409},{"url":408,"text":409},[],[863,864,865,866,867,868,869,870,871,872],{"url":834,"text":352},{"url":836,"text":361},{"url":838,"text":364},{"url":840,"text":841},{"url":843,"text":808},{"url":378,"text":379},{"url":378,"text":381},{"url":378,"text":383},{"url":385,"text":386},{"url":385,"text":388},"Gemini 3.1 Pro : Avis, Prix, Info & 29 IA Alternatives | 2026 | AIxploria \nTop 10 Catégories IA Tutoriels IA + Plus Nouveaux Outils IA Outils IA Gratuits Actualités IA 2026 Bonus & Extras Soumettre une IA \nEnglish Français Español Deutsch Italiano Português + More × 中文 日本語 العربية हिन्दी Русский 한국어 Türkçe Tiếng Việt فارسی বাংলা Indonesia ไทย Nederlands Polski اردو Kiswahili Українська Ελληνικά עברית Svenska Norsk Dansk Suomi Čeština Magyar Melayu Tagalog 中文 (香港) தமிழ் \nS'inscrire \nTop 10 Catégories IA Tutoriels IA + Plus Nouveaux Outils IA Outils IA Gratuits Actualités IA 2026 Bonus & Extras Soumettre une IA \nS'inscrire \nAccueil IA Récentes Gemini 3.1 Pro \nGemini 3.1 Pro\n#9 dans Modèles LLM \n4.7\u002F5 \nVisiter ce site \n« Un modèle IA de Google capable de gérer des tâches complexes avec du texte, des images, de l'audio ou de la vidéo. Il peut résumer de longues sources, analyser des données et aider à coder des interfaces complètes»\nEn savoir plus : \nChatGPT \nPerplexity \nOutil Vérifié \nPayant \n159578 \nLiens utiles \nSite Officiel \nCarte modèle \nDoc API \n#IA Récentes \n#Modèles LLM \n101 ★ Outils IA de Prestige ★ \n★ Mis en avant \nEmergent AI \n« Transformez vos idées en applications complètes grâce à des agents IA qui codent, testent et déploient pour vous. Créez sites web (full-stack), apps IA, jeux et outils automatisés simplement en décrivant votre projet »\nVisiter \n★ Mis en avant \nClickUp \n« Gérez vos projets plus vite avec une plateforme qui regroupe équipes, tâches et documents en un seul espace. Son IA intégrée résume vos notes, rédige pour vous et organise automatiquement vos priorités »\nVisiter \n★ Mis en avant \nThumbnailCreator.com \n« Créez rapidement des miniatures accrocheuses pour vos vidéos sur YouTube, Twitch et Facebook. Attirez plus de vues et améliorez facilement le taux de clics sur vos clip vidéos »\nVisiter \n★ Mis en avant \nVmake Video Enhancer \n« Améliorez vos vidéos floues et de mauvaise qualité pour les transformer en HD et 4K grâce à l'IA. Vmake accentue les détails de vos vidéos, améliore leur netteté et leur donne un aspect plus professionnel en quelques secondes »\nVisiter \nIA Alternatives à \nGemini 3.1 Pro \nFreemium Kimi K3 \n4.4\u002F5 \n« Le modèle LLM de chez Moonshot qui rivalise avec les meilleurs modèles propriétaires sur de nombreux benchmarks. Conçu pour le codage longue durée, le raisonnement avancé et les tâches de knowledge work, c'est aussi le premier modèle open source à franchir la barre des 2,8 trillions de paramètres»\n242 Visiter Payant Claude Opus 4.6 \n4.6\u002F5 \n« Le modèle le plus puissant d'Anthropic qui peut gérer des contextes d'1 million de tokens et raisonne de manière adaptative. Il coordonne plusieurs agents en parallèle pour résoudre des tâches complexes. Vous contrôlez le niveau d'effort, l'intelligence ou le coût avec 4 réglages disponibles»\n755 Visiter Payant Claude Opus 5 \n4.4\u002F5 \n« Un modèle haut de gamme pensé pour le travail quotidien, avec de meilleures capacités en code, en raisonnement et en tâches agentiques, tout en restant moins coûteux que le niveau supérieur. Disponible sur les plans Pro et Max chez Anthropic»\n141 Visiter Payant GPT‑5.5 \n4.6\u002F5 \n« Résolvez des tâches longues et complexes en laissant l’IA planifier, utiliser des outils, vérifier son travail et aller jusqu’au bout avec moins de supervision. Ce modèle améliore nettement le codage, l’usage d’ordinateurs virtuels, la recherche en ligne et les missions de knowledge work»\n213 Visiter Payant Claude Opus 4.8 \n4.4\u002F5 \n« Exploitez le modèle IA d'Anthropic, plus fiable, plus honnête et 4 fois moins enclin à laisser passer ses propres erreurs. Son mode rapide délivre jusqu'à 2,5x plus de tokens par seconde (au même tarif API que la version précédente)\n»\n248 Visiter Gratuit Gemini 3.6 Flash \n4.4\u002F5 \n« Conçu pour les tâches intensives, ce modèle de la famille Gemini de Google surpasse son prédécesseur en efficacité de tokens tout en améliorant les performances en codage et en travail de connaissance. Avec un contexte d’1 million de tokens, une multimodalité complète et une meilleure efficacité sur les tâches agentiques»\n98 Visiter Payant GPT-5.4 \n4.4\u002F5 \n« Le modèle frontier le plus puissant d'OpenAI contrôle directement votre ordinateur, traite 1 million de tokens et raisonne sur des tâches très longues. Annoncé comme réduisant les erreurs de 33 % et améliorant considérablement le code et l'analyse des documents »\n189 Visiter Payant Claude Opus 4.7 \n4.5\u002F5 \n« Le dernier modèle haut de gamme de chez Anthropic optimisé pour le code agentique, les tâches longues et les scénarios critiques en entreprise. Avec auto‑vérification des réponses et fenêtre de contexte d’un million de tokens. Il est également capable d'ajuster automatiquement la profondeur de sa réflexion selon la difficulté du problème»\n162 Visiter \n+ Voir plus d'IA \nCopier le code \nL'intelligence artificielle pour tous\nRessources Tutoriels, astuces et blog Agenda des conférences sur l'IA Glossaire des IA Emplois dans l'IA Newsletter Services pratiques Chaînes YouTube sur l'IA Top 100 IA Liste GPTs Outils IA Hubspot Meilleurs Agents IA Entreprise Ajouter un outil IA Annoncer Mettre à jour votre site Sponsorship ★ Media Kit Qui sommes-nous ? Contactez-nous \n© 2026 AIxploria. Tous droits réservés \nPolitique des Cookies \nConditions d'utilisations Mentions Légales Gérer le consentement aux cookies \nAfin de fournir une expérience optimale, Aixploria utilise des technologies telles que les cookies pour stocker et\u002Fou accéder aux informations relatives à l'appareil. 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Le fait de ne pas consentir ou de retirer son consentement peut avoir un effet négatif sur certaines caractéristiques et fonctions. \nFonctionnel \nFonctionnel \nToujours activé \nLe stockage ou l’accès technique est strictement nécessaire dans la finalité d’intérêt légitime de permettre l’utilisation d’un service spécifique explicitement demandé par l’abonné ou l’internaute, ou dans le seul but d’effectuer la transmission d’une communication sur un réseau de communications électroniques. \nPréférences \nPréférences \nLe stockage ou l’accès technique est nécessaire dans la finalité d’intérêt légitime de stocker des préférences qui ne sont pas demandées par l’abonné ou la personne utilisant le service. \nStatistiques \nStatistiques \nLe stockage ou l’accès technique qui est utilisé exclusivement à des fins statistiques. \nLe stockage ou l’accès technique qui est utilisé exclusivement dans des finalités statistiques anonymes. En l’absence d’une assignation à comparaître, d’une conformité volontaire de la part de votre fournisseur d’accès à internet ou d’enregistrements supplémentaires provenant d’une tierce partie, les informations stockées ou extraites à cette seule fin ne peuvent généralement pas être utilisées pour vous identifier. \nMarketing \nMarketing \nLe stockage ou l’accès technique est nécessaire pour créer des profils d’internautes afin d’envoyer des publicités, ou pour suivre l’internaute sur un site web ou sur plusieurs sites web ayant des finalités marketing similaires. \nGérer les options \nGérer les services \nGérer {vendor_count} fournisseurs \nEn savoir plus sur ces finalités \nAccepter \nTout refuser \nPersonnaliser \nSauvegarder les préférences \nPersonnaliser \n{title} \n{title} \n{title} Gérer le consentement Gérer le consentement","2026-08-09T00:27:47.350Z","b03320c7535c9c80b9b388b1706232ca06c94031048b735ee805d497286248a5","data\u002Fcatalog\u002Fcache\u002Faixploria\u002Fd1f227d5381c7d0c389f0c0509b5e52e6dac55308732acf42c333f2819c3824c.html",{"url":840,"final_url":840,"status":425,"fetched_at":878,"content_hash":879,"title":880,"description":352,"headings":881,"pricing_evidence":920,"privacy_evidence":925,"capability_evidence":932,"text_excerpt":935,"linked_pages":936,"research_links":993,"blog_titles":998,"use_case_evidence":999,"feature_evidence":1000},"2026-08-09T01:05:24.307Z","10ef70837e480087aba7de637be0aa2a9be505a8493aa72331b37e549a55e3a7","Gemini 3.1 Pro - Model Card — Google DeepMind",[882,883,884,885,882,883,884,885,800,886,887,888,889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919],"Explore our next generation AI systems","Our latest AI breakthroughs and updates from the lab","Unlocking a new era of discovery with AI","Our mission is to build AI responsibly to benefit humanity","Model Information","Description","Model dependencies","Inputs","Outputs","Architecture","Model Data","Training Dataset","Training Data Processing","Implementation and Sustainability","Hardware","Software","Distribution","Evaluation","Approach","Results","Intended Usage and Limitations","Benefit and Intended Usage","Known Limitations","Acceptable Usage","Ethics and Content Safety","Evaluation Approach","Safety Policies","Training and Development Evaluation Results","Human Red Teaming Results","Risks and Mitigations","Frontier Safety","Latest model cards","Gemini Robotics ER 2","Gemini Robotics On-Device 2","Lyria 3.5","Gemini 3.6 Flash","Gemini 3.5 Flash-Lite","Gemini 3.1 Flash-Lite Image",[921,922,902,923,924],"Learn more View PDF version Model Cards are intended to provide essential information on Gemini models, including known limitations, mitigation approaches, and safety performance. Model cards may be updated from time-to-time; for example, to include updated evaluations as the model is improved or revised.","Model Information Model Data Implementation and Sustainability Distribution Evaluation Intended Usage and Limitations Ethics and Content Safety Frontier Safety Model Information","agentic performance advanced coding long context and\u002For multimodal understanding algorithmic development Known Limitations","For more information about the known limitations for Gemini 3.1 Pro, see the Gemini 3 Pro model card .",[926,927,893,928,894,929,909,930,931],"About Google DeepMind Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Models Explore our next generation AI systems","About Google DeepMind Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Build with Gemini Try Gemini Google DeepMind Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Google Research Explore our research Products and apps Gemini app Chat with Gemini Google AI Studio Build with our next-gen AI models Google Antigravity Our agentic development platform Models Research Science About Build with Gemini Try Gemini Published 19 February 2026 Gemini 3.1 Pro","Gemini 3.1 Pro is based on Gemini 3 Pro. For more information about the training dataset for Gemini 3.1 Pro, see the Gemini 3 Pro model card .","For more information about the training data processing for Gemini 3.1 Pro, see the Gemini 3 Pro model card .","Learn more Follow us Sign up for updates on our latest innovations I accept Google's Terms and Conditions and acknowledge that my information will be used in accordance with Google's Privacy Policy .","Sign up Build AI responsibly to benefit humanity Models Gemini Gemini Omni Nano Banana Gemini Audio Gemma Genie Lyria Veo Research Gemini Robotics Breakthroughs Evals Publications Frontier safety Responsibility Science AlphaFold AlphaGenome WeatherNext AlphaEarth AlphaEvolve Products Gemini app Google AI Studio Google Antigravity Learn more About News Careers National Partnerships for AI Accelerator programs The Podcast About Google Google products Privacy Terms Cookies management controls",[927,933,934],"Gemini App Google Cloud \u002F Vertex AI Google AI Studio Gemini API Google Antigravity Gemini Enterprise NotebookLM Our models are available to downstream providers via an application program interface (API) and subject to relevant terms of use. There is no required hardware or software to use the model. For AI Studio and Gemini API, see the Gemini API Additional Terms of Service ; for Vertex AI, see Google Cloud Platform Terms of Service . For more information, see Gemini Model API instructions and Gemini API in Vertex AI quickstart .","vs. Gemini 3 Pro Text to Text Safety Automated content safety evaluation measuring safety policies +0.10% (non-egregious) Multilingual Safety Automated safety policy evaluation across multiple languages +0.11% (non-egregious) Image to Text Safety Automated content safety evaluation measuring safety policies -0.33% Tone 2 Automated evaluation measuring objective tone of model refusal +0.02% Unjustified-refusals Automated evaluation measuring model’s ability to respond to borderline prompts while remaining safe -0.08% 1 The ordering of evaluations in this table has changed from previous iterations of the 2.5 Flash-Lite model card in order to list safety evaluations together and improve readability. The type of evaluations listed have remained the same.","Gemini 3.1 Pro - Model Card — Google DeepMind Skip to main content Explore our next generation AI systems\nExplore models Gemini Gemini Build intelligent agents Gemini Omni Create anything from anything Nano Banana Create and edit detailed images Gemini Audio Talk, create and control audio Specialized models Veo Generate cinematic video with audio Imagen Generate high-quality images from text Lyria Generate high fidelity music and audio World models & physical AI Genie 3 Generate and explore interactive worlds Gemini Robotics Perceive, reason, use tools and interact Open models Gemma Build responsible AI applications at scale Our latest AI breakthroughs and updates from the lab\nExplore research Breakthroughs SIMA 2 An agent that plays, reasons, and learns with you Genie 3 Generate and explore interactive worlds AlphaGo Mastering the game of Go Gemini Robotics Perceive, reason, use tools and interact Learn more Evals Publications Responsibility Frontier safety Unlocking a new era of discovery with AI\nExplore science Breakthroughs AlphaFold Predict protein structures with high accuracy WeatherNext Fast and accurate AI weather forecasting AlphaEarth Map our planet in unprecedented detail AlphaEvolve Design advanced algorithms for math and applications in computing Learn more Gemini for Science Experimental Tools Science Skills Our mission is to build AI responsibly to benefit humanity\nAbout Google DeepMind Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Models Explore our next generation AI systems\nExplore models Gemini Gemini Build intelligent agents Gemini Omni Create anything from anything Nano Banana Create and edit detailed images Gemini Audio Talk, create and control audio Specialized models Veo Generate cinematic video with audio Imagen Generate high-quality images from text Lyria Generate high fidelity music and audio World models & physical AI Genie 3 Generate and explore interactive worlds Gemini Robotics Perceive, reason, use tools and interact Open models Gemma Build responsible AI applications at scale Research Our latest AI breakthroughs and updates from the lab\nExplore research Breakthroughs SIMA 2 An agent that plays, reasons, and learns with you Genie 3 Generate and explore interactive worlds AlphaGo Mastering the game of Go Gemini Robotics Perceive, reason, use tools and interact Learn more Evals Publications Responsibility Frontier safety Science Unlocking a new era of discovery with AI\nExplore science Breakthroughs AlphaFold Predict protein structures with high accuracy WeatherNext Fast and accurate AI weather forecasting AlphaEarth Map our planet in unprecedented detail AlphaEvolve Design advanced algorithms for math and applications in computing Learn more Gemini for Science Experimental Tools Science Skills About Our mission is to build AI responsibly to benefit humanity\nAbout Google DeepMind Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Build with Gemini Try Gemini Google DeepMind Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Google Research Explore our research Products and apps Gemini app Chat with Gemini Google AI Studio Build with our next-gen AI models Google Antigravity Our agentic development platform Models Research Science About Build with Gemini Try Gemini Published 19 February 2026 Gemini 3.1 Pro\nLearn more View PDF version Model Cards are intended to provide essential information on Gemini models, including known limitations, mitigation approaches, and safety performance. Model cards may be updated from time-to-time; for example, to include updated evaluations as the model is improved or revised.\nPublished: February 2026\nModel Information Model Data Implementation and Sustainability Distribution Evaluation Intended Usage and Limitations Ethics and Content Safety Frontier Safety Model Information\nDescription\nGemini 3.1 Pro is the next iteration in the Gemini 3 series of models, a suite of highly capable, natively multimodal reasoning models. As of this model card’s date of publication, Gemini 3.1 Pro is Google’s most advanced model for complex tasks. Gemini 3.1 Pro can comprehend vast datasets and challenging problems from massively multimodal information sources, including text, audio, images, video, and entire code repositories.\nModel dependencies\nGemini 3.1 Pro is based on Gemini 3 Pro.\nInputs\nText strings (e.g., a question, a prompt, document(s) to be summarized), images, audio, and video files, with a token context window of up to 1M.\nOutputs\nText, with a 64K token output.\nArchitecture\nGemini 3.1 Pro is based on Gemini 3 Pro. For more information about the model architecture for Gemini 3.1 Pro, see the Gemini 3 Pro model card .\nModel Data\nTraining Dataset\nGemini 3.1 Pro is based on Gemini 3 Pro. For more information about the training dataset for Gemini 3.1 Pro, see the Gemini 3 Pro model card .\nTraining Data Processing\nFor more information about the training data processing for Gemini 3.1 Pro, see the Gemini 3 Pro model card .\nImplementation and Sustainability\nHardware\nGemini 3.1 Pro is based on Gemini 3 Pro. For more information about the hardware for Gemini 3.1 Pro and our continued commitment to operate sustainably , see the Gemini 3 Pro model card .\nSoftware\nGemini 3.1 Pro is based on Gemini 3 Pro. For more information about the software for Gemini 3.1 Pro, see the Gemini 3 Pro model card .\nDistribution\nGemini 3.1 Pro is distributed in the following channels; respective documentation shared in line:\nGemini App Google Cloud \u002F Vertex AI Google AI Studio Gemini API Google Antigravity Gemini Enterprise NotebookLM Our models are available to downstream providers via an application program interface (API) and subject to relevant terms of use. There is no required hardware or software to use the model. For AI Studio and Gemini API, see the Gemini API Additional Terms of Service ; for Vertex AI, see Google Cloud Platform Terms of Service . For more information, see Gemini Model API instructions and Gemini API in Vertex AI quickstart .\nEvaluation\nApproach\nGemini 3.1 Pro was evaluated across a range of benchmarks, including reasoning, multimodal capabilities, agentic tool use, multi-lingual performance, and long-context. Additional benchmarks and details on approach, results and their methodologies can be found at: deepmind.google\u002Fmodels\u002Fevals-methodology\u002Fgemini-3-1-pro .\nResults\nGemini 3.1 Pro significantly outperforms Gemini 3 Pro across a range of benchmarks requiring enhanced reasoning and multimodal capabilities. Results as of February 2026 are listed below:\nBenchmark Notes Gemini 3.1 Pro Thinking (High) Gemini 3 Pro Thinking (High) Sonnet 4.6 Thinking (Max) Opus 4.6 Thinking (Max) GPT-5.2 Thinking (xhigh) GPT-5.3-Codex Thinking (xhigh) Humanity's Last Exam Academic reasoning (full set, text + MM) No tools 44.4% 37.5% 33.2% 40.0% 34.5% — Search (blocklist) + Code 51.4% 45.8% 49.0% 53.1% 45.5% — ARC-AGI-2 Abstract reasoning puzzles ARC Prize Verified 77.1% 31.1% 58.3% 68.8% 52.9% — GPQA Diamond Scientific knowledge No tools 94.3% 91.9% 89.9% 91.3% 92.4% — Terminal-Bench 2.0 Agentic terminal coding Terminus-2 harness 68.5% 56.9% 59.1% 65.4% 54.0% 64.7% Other best self-reported harness — — — — 62.2% (Codex) 77.3% (Codex) SWE-Bench Verified Agentic coding Single attempt 80.6% 76.2% 79.6% 80.8% 80.0% — SWE-Bench Pro (Public) Diverse agentic coding tasks Single attempt 54.2% 43.3% — — 55.6% 56.8% LiveCodeBench Pro Competitive coding problems from Codeforces, ICPC, and IOI Elo 2887 2439 — — 2393 — SciCode Scientific research coding 59% 56% 47% 52% 52% — APEX-Agents Long horizon professional tasks 33.5% 18.4% — 29.8% 23.0% — GDPval-AA Elo Expert tasks 1317 1195 1633 1606 1462 — τ2-bench Agentic and tool use Retail 90.8% 85.3% 91.7% 91.9% 82.0% — Telecom 99.3% 98.0% 97.9% 99.3% 98.7% — MCP Atlas Multi-step workflows using MCP 69.2% 54.1% 61.3% 59.5% 60.6% — BrowseComp Agentic search Search + Python + Browse 85.9% 59.2% 74.7% 84.0% 65.8% — MMMU-Pro Multimodal understanding and reasoning No tools 80.5% 81.0% 74.5% 73.9% 79.5% — MMMLU Multilingual Q&A 92.6% 91.8% 89.3% 91.1% 89.6% — MRCR v2 (8-needle) Long context performance 128k (average) 84.9% 77.0% 84.9% 84.0% 83.8% — 1M (pointwise) 26.3% 26.3% Not supported Not supported Not supported — Methodology: deepmind.google\u002Fmodels\u002Fevals-methodology\u002Fgemini-3-1-pro \nIntended Usage and Limitations\nBenefit and Intended Usage\nGemini 3.1 Pro is the next iteration in the Gemini 3 series of models, a suite of highly intelligent and adaptive models, capable of helping with real-world complexity, solving problems that require enhanced reasoning and intelligence, creativity, strategic planning and making improvements step-by-step. It is particularly well-suited for applications that require:\nagentic performance advanced coding long context and\u002For multimodal understanding algorithmic development Known Limitations\nFor more information about the known limitations for Gemini 3.1 Pro, see the Gemini 3 Pro model card .\nAcceptable Usage\nFor more information about the acceptable usage for Gemini 3.1 Pro, see the Gemini 3 Pro model card .\nEthics and Content Safety\nEvaluation Approach\nFor more information about the evaluation approach for Gemini 3.1 Pro, see the Gemini 3 Pro model card .\nSafety Policies\nFor more information about the safety policies for Gemini 3.1 Pro, see the Gemini 3 Pro model card .\nTraining and Development Evaluation Results\nResults for some of the internal safety evaluations conducted during the development phase are listed below. The evaluation results are for automated evaluations and not human evaluation or red teaming. Scores are provided as an absolute percentage increase or decrease in performance compared to the indicated model, as described below. Overall, Gemini 3.1 Pro outperforms Gemini 3 Pro across both safety and tone, while keeping unjustified refusals low. We mark improvements in green and regressions in red. Safety evaluations of Gemini 3.1 Pro produced results consistent with the original Gemini 3 Pro safety assessment.\nEvaluation 1 Description Gemini 3.1 Pro\nvs. Gemini 3 Pro Text to Text Safety Automated content safety evaluation measuring safety policies +0.10% (non-egregious) Multilingual Safety Automated safety policy evaluation across multiple languages +0.11% (non-egregious) Image to Text Safety Automated content safety evaluation measuring safety policies -0.33% Tone 2 Automated evaluation measuring objective tone of model refusal +0.02% Unjustified-refusals Automated evaluation measuring model’s ability to respond to borderline prompts while remaining safe -0.08% 1 The ordering of evaluations in this table has changed from previous iterations of the 2.5 Flash-Lite model card in order to list safety evaluations together and improve readability. The type of evaluations listed have remained the same.\n2 For tone and instruction following, a positive percentage increase represents an improvement in the tone of the model on sensitive topics and the model’s ability to follow instructions while remaining safe compared to Gemini 2.5 Pro. We mark improvements in green and regressions in red.\nWe continue to improve our internal evaluations, including refining automated evaluations to reduce false positives and negatives, as well as update query sets to ensure balance and maintain a high standard of results. The performance results reported below are computed with improved evaluations and thus are not directly comparable with performance results found in previous Gemini model cards.\nWe expect variation in our automated safety evaluations results, which is why we review flagged content to check for egregious or dangerous material. Our manual review confirmed losses were overwhelmingly either a) false positives or b) not egregious.\nHuman Red Teaming Results\nWe conduct manual red teaming by specialist teams who sit outside of the model development team. High-level findings are fed back to the model team. For child safety evaluations, Gemini 3.1 Pro satisfied required launch thresholds, which were developed by expert teams to protect children online and meet Google’s commitments to child safety across our models and Google products. For content safety policies generally, including child safety, we saw similar safety performance compared to Gemini 3 Pro.\nRisks and Mitigations\nFor more information about the risks and mitigations for Gemini 3.1 Pro, see the Gemini 3 Pro model card .\nFrontier Safety\nOur Frontier Safety Framework includes rigorous evaluations that address risks of severe harm from frontier models, covering five risk domains: CBRN (chemical, biological, radiological and nuclear information risks), cyber, harmful manipulation, machine learning R&D and misalignment.\nOur frontier safety strategy is based on a “safety buffer” to prevent models from reaching critical capability levels (CCLs), i.e. if a frontier model does not reach the alert threshold for a CCL, we can assume models developed before the next regular testing interval will not reach that CCL. We conduct continuous testing, evaluating models at a fixed cadence and when a significant capability jump is detected. (Read more about this in our approach to technical AGI safety. )\nFollowing FSF protocols, we conducted a full evaluation of Gemini 3.1 Pro (focusing on Deep Think mode). We found that the model remains below alert thresholds for the CBRN, harmful manipulation, machine learning R&D, and misalignment CCLs. As previous models passed the alert threshold for cyber, we performed more additional testing in this domain on Gemini 3.1 Pro with and without Deep Think mode, and found that the model remains below the cyber CCL.\nMore details on our evaluations and the mitigations we deploy can be found in the Gemini 3 Pro Frontier Safety Framework Report .\nDomain Key Results for Gemini 3.1 Pro CCL CCL reached? CBRN (Deep Think mode) The model can provide highly accurate and actionable information but still fails to offer novel or sufficiently complete and detailed instructions for critical stages, to significantly enhance the capabilities of low to medium resourced threat actors required for the CCL. We continue to deploy mitigations in this domain. Uplift Level 1 CCL not reached Cyber (3.1 Pro) We conducted additional testing on the model in this domain as Gemini 3 Pro had previously reached the alert threshold. The model shows an increase in cyber capabilities compared to Gemini 3 Pro. As with Gemini 3 Pro, the model has reached the alert threshold, but still does not reach the levels of uplift required for the CCL. \n(Deep Think mode) Accounting for inference costs, the model with Deep Think mode performs considerably worse than without Deep Think mode. Even at high levels of inference, results for the model with Deep Think mode do not suggest higher capability than without Deep Think mode. \nWe continue to deploy mitigations in this domain. Uplift Level 1 CCL not reached Harmful Manipulation (Deep Think mode) Evaluations indicated that the model showed higher manipulative efficacy for belief change metrics compared to a non-AI baseline, with the maximum odds ratio of 3.6x, which is the same as Gemini 3 Pro, and did not reach the alert threshold. Level 1 (exploratory) CCL not reached Machine Learning R&D (Deep Think mode) The model shows gains on RE-Bench compared to Gemini 3 Pro, with a human-normalised average score of 1.27 compared to Gemini 3 Pro's score of 1.04. On one particular challenge, Optimise LLM Foundry, it scores double the human-normalised baseline score (reducing the runtime of a fine-tuning script from 300 seconds to 47 seconds, compared to the human reference solution of 94 seconds). However, the model’s average performance across all challenges remains beneath the alert threshold for the CCLs. Acceleration level 1\nAutomation level 1 CCLs not reached Misalignment (Exploratory) (Deep Think mode) On stealth evaluations, the model performs similarly to Gemini 3 Pro. On situational awareness, the model is stronger than Gemini 3 Pro: on three challenges which no other model has been able to consistently solve, max tokens, context size mod, and oversight frequency, the model achieves a success rate of almost 100%. However, its performance on other challenges is inconsistent, and thus the model does not reach the alert threshold. Instrumental Reasoning\nLevels 1 + 2 (exploratory) CCLs not reached Latest model cards\nGemini Robotics ER 2\nLearn more Gemini Robotics On-Device 2\nLearn more Lyria 3.5\nLearn more Gemini 3.6 Flash\nLearn more Gemini 3.5 Flash-Lite\nLearn more Gemini 3.1 Flash-Lite Image\nLearn more Follow us Sign up for updates on our latest innovations I accept Google's Terms and Conditions and acknowledge that my information will be used in accordance with Google's Privacy Policy .\nSign up Build AI responsibly to benefit humanity Models Gemini Gemini Omni Nano Banana Gemini Audio Gemma Genie Lyria Veo Research Gemini Robotics Breakthroughs Evals Publications Frontier safety Responsibility Science AlphaFold AlphaGenome WeatherNext AlphaEarth AlphaEvolve Products Gemini app Google AI Studio Google Antigravity Learn more About News Careers National Partnerships for AI Accelerator programs The Podcast About Google Google products Privacy Terms Cookies management controls",[937,947,956,966,976,985],{"url":938,"kind":939,"title":940,"description":352,"feature_evidence":941,"use_case_evidence":944,"fetched_at":945,"status":425,"content_hash":946},"https:\u002F\u002Fdeepmind.google\u002Fmodels\u002Fveo\u002Fprompt-guide\u002F","other","How to create effective prompts with Veo 3 — Google DeepMind",[942,943],"About Google DeepMind Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Build with Gemini Try Gemini Google DeepMind Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Google Research Explore our research Products and apps Gemini app Chat with Gemini Google AI Studio Build with our next-gen AI models Google Antigravity Our agentic development platform Models Research Science About Build with Gemini Try Gemini Veo How to create effective prompts with Veo 3","Use evocative, sensory language to paint a picture of your imaginary world. Is it familiar, fantastical – a mix of both? Think about light, texture, and atmosphere – and think about who lives here.",[],"2026-08-09T01:05:22.589Z","1194fe1522ac39b92cbbf70036fc1acef3d54f2a4bee346c19cb180cdf8c8ff0",{"url":948,"kind":939,"title":949,"description":352,"feature_evidence":950,"use_case_evidence":953,"fetched_at":954,"status":425,"content_hash":955},"https:\u002F\u002Fdeepmind.google\u002Fmodels\u002Flyria\u002Fprompt-guide\u002F","How to create effective prompts with Lyria — Google DeepMind",[951,952],"About Google DeepMind Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Build with Gemini Try Gemini Google DeepMind Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Google Research Explore our research Products and apps Gemini app Chat with Gemini Google AI Studio Build with our next-gen AI models Google Antigravity Our agentic development platform Models Research Science About Build with Gemini Try Gemini Lyria How to create effective prompts with Lyria","Create a vocal profile Do you want a male or female singer? Do they have commanding baritone vocals, or a clear and high soprano range? Is their voice rich, gravelly, soulful, breathy? And are they singing in English – or another language?",[],"2026-08-09T01:05:22.898Z","367542f366175e3853bf9fbff322b208bdae81493b0a19edf4383fd8ce0fd98d",{"url":957,"kind":939,"title":958,"description":352,"feature_evidence":959,"use_case_evidence":962,"fetched_at":964,"status":425,"content_hash":965},"https:\u002F\u002Fdeepmind.google\u002Fmodels\u002Fgemini-image\u002Fprompt-guide\u002F","How to create effective image prompts with Nano Banana — Google DeepMind",[960,961],"About Google DeepMind Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Build with Gemini Try Gemini Google DeepMind Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Google Research Explore our research Products and apps Gemini app Chat with Gemini Google AI Studio Build with our next-gen AI models Google Antigravity Our agentic development platform Models Research Science About Build with Gemini Try Gemini Nano Banana How to create effective image prompts with Gemini Image","Write in one language, then specify the target language and regional cultural cues in your prompt. If you need specific foreign phrasing, provide the exact translated text in quote marks to guide the rendering.",[963],"Prompt for your desired aspect ratio (like 4:3, 1:1, or 9:16) and describe the image format you need – for example, a \"widescreen backdrop,\" or a \"vertical social post\". Look for the in-product resolution dropdowns to upscale to 2K or 4K.","2026-08-09T01:05:23.177Z","7ac902226d34717611a4de1df28507fa9adaa495deb396df0b0e092168b8a71d",{"url":967,"kind":939,"title":968,"description":352,"feature_evidence":969,"use_case_evidence":973,"fetched_at":974,"status":425,"content_hash":975},"https:\u002F\u002Fdeepmind.google\u002Fmodels\u002Fgenie\u002Fprompt-guide\u002F","How to create effective prompts with Genie 3 — Google DeepMind",[970,971,972],"About Google DeepMind Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Build with Gemini Try Gemini Google DeepMind Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Google Research Explore our research Products and apps Gemini app Chat with Gemini Google AI Studio Build with our next-gen AI models Google Antigravity Our agentic development platform Models Research Science About Build with Gemini Try Gemini Genie How to create effective prompts with Genie","Maintain direct, simple and action-oriented language","Describe the way the environment and character feel to evoke the mood you want. Is the environment dimly lit with mysterious smoke on the ground? Does the character walk as if floating like a feather? Using game-like language can help build richer environments with more precise character control.",[],"2026-08-09T01:05:23.479Z","9785cc4b9358d4ccdfe819b31faf14969963df6c93b1179f2cd7b80bf66d6ac7",{"url":977,"kind":939,"title":978,"description":352,"feature_evidence":979,"use_case_evidence":982,"fetched_at":983,"status":425,"content_hash":984},"https:\u002F\u002Fdeepmind.google\u002Fmodels\u002Fgemini-omni\u002Fprompt-guide\u002F","How to create effective prompts with Gemini Omni — Google DeepMind",[980,981],"About Google DeepMind Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Build with Gemini Try Gemini Google DeepMind Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Google Research Explore our research Products and apps Gemini app Chat with Gemini Google AI Studio Build with our next-gen AI models Google Antigravity Our agentic development platform Models Research Science About Build with Gemini Try Gemini Gemini Omni How to create effective prompts with Gemini Omni","Think of Gemini Omni like Nano Banana – but for video. Build and fine-tune your creation at any step with natural language.",[],"2026-08-09T01:05:23.857Z","af28dae4797a4ff0044ec5cc0ddf9302c3d906a60aa61a00249dee7e11d34e0b",{"url":986,"kind":613,"title":987,"description":352,"feature_evidence":988,"use_case_evidence":990,"fetched_at":991,"status":425,"content_hash":992},"https:\u002F\u002Fdeepmind.google\u002Fresearch\u002F","Research — Google DeepMind",[989],"About Google DeepMind Learn more Education Our National Partnerships for AI Accelerator programs The Podcast Responsibility Ensuring AI safety through proactive security, even against evolving threats News Discover our latest AI breakthroughs, projects, and updates Careers We’re looking for people who want to make a real, positive impact on the world Build with Gemini Try Gemini Google DeepMind Google AI Learn about all our AI Google DeepMind Explore the frontier of AI Google Labs Try our AI experiments Google Research Explore our research Products and apps Gemini app Chat with Gemini Google AI Studio Build with our next-gen AI models Google Antigravity Our agentic development platform Models Research Science About Build with Gemini Try Gemini Research We work on some of the most complex and interesting \u2028challenges in AI",[],"2026-08-09T01:05:24.156Z","8f15c51051a1429000863139a35d21d523515e699fe79fd6834517d67edc57cf",[994,997],{"url":995,"title":996,"kind":613},"https:\u002F\u002Fresearch.google\u002F?utm_source=deepmind.google&utm_medium=referral&utm_campaign=gdm&utm_content=","Google Research Explore our research",{"url":986,"title":987,"kind":613},[],[],[927,933,934],"tak",[],"Brak potwierdzonego regionu danych w zebranych materiałach; dla produktów Google Cloud możliwa jest konfiguracja regionów EU w Vertex AI, ale nie potwierdzono tego dla tej konkretnej ścieżki dostępu wprost dla Gemini 3.1 Pro","Nie ustalono jednoznacznie dla tego modelu z zebranych źródeł; w oficjalnych warunkach Google dla usług płatnych dane nie są używane do ulepszania produktów, natomiast dla usług bezpłatnych \u002F unpaid mogą być używane do poprawy i rozwoju produktów Google",[1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018],"natywnie multimodalny model rozumowania","obsługa tekstu, audio, obrazów, wideo i całych repozytoriów kodu","kontekst do 1M tokenów","wyjście tekstowe do 64K tokenów","mocne rozumowanie, kodowanie, zrozumienie multimodalne i działanie agentowe","dostęp przez Gemini App, Google Cloud \u002F Vertex AI, Google AI Studio, Gemini API, Google Antigravity, Gemini Enterprise i NotebookLM","Multimodal understanding (text, audio, images, video)","Long context processing (up to 1M tokens)","Advanced coding assistance","Agentic performance","Multilingual capabilities","High-level reasoning and problem-solving","Support for complex tasks involving large datasets","Częściowo: dla Gemini API w EEA\u002FCH\u002FUK obowiązują warunki Paid Services dla wszystkich usług, w tym Google AI Studio i unpaid quota; dla Vertex AI oraz usług korporacyjnych Google podaje mocniejsze podstawy zgodności i opcje regionów EU",[1021,1022,1023,1024,1025,1026,1027],"Gemini App","Google Cloud \u002F Vertex AI","Google AI Studio","Gemini API","Google Antigravity","Gemini Enterprise","NotebookLM",[1029,1030],"angielski","wielojęzyczny",[1032,1033,1034,1035,1036],"Karta modelu odsyła do karty Gemini 3 Pro po szczegóły znanych ograniczeń, akceptowalnego użycia, bezpieczeństwa i ryzyk","Nie ma w zebranych danych pełnej, samodzielnej listy ograniczeń dla Gemini 3.1 Pro","Model jest zależny od Gemini 3 Pro","Known limitations are documented in the Gemini 3 Pro model card","Image-to-text safety evaluation showed a slight regression (-0.33%)",[800,1038],"Gemini 3 Pro",[1040,1041],{"plan_name":680,"access_model":680,"price":680,"currency":680,"billing_period":680,"included_usage":680,"trial_days":680,"card_required":680,"source_evidence":680},{"plan_name":680,"access_model":1042,"price":680,"currency":680,"billing_period":680,"included_usage":680,"trial_days":680,"card_required":680,"source_evidence":840},"official","W zebranych źródłach oficjalnych nie udało się potwierdzić publicznej ceny startowej ani pełnej tabeli planów cenowych dla Gemini 3.1 Pro; w wynikach wtórnych pojawia się model płatny i stawki API, ale nie stanowią one wystarczająco pewnego potwierdzenia do wpisania jako zweryfikowany fakt.","Dla usług płatnych Google wskazuje, że dane nie są używane do ulepszania produktów; dla usług bezpłatnych \u002F unpaid Google może używać treści do poprawy i rozwoju produktów i technologii uczenia maszynowego. W EEA\u002FCH\u002FUK warunki Paid Services stosują się do wszystkich usług, także Google AI Studio i unpaid quota w Gemini API.","19 lutego 2026",[1047,1048],{"kind":613,"title":996,"url":995},{"kind":613,"title":987,"url":986},"Oficjalna karta modelu potwierdza, że Gemini 3.1 Pro został opublikowany 19 lutego 2026, jest następną iteracją w serii Gemini 3 i Google opisuje go jako swój najbardziej zaawansowany model do złożonych zadań. Model przyjmuje tekst, obrazy, audio i wideo, obsługuje kontekst do 1M tokenów, ma wyjście tekstowe do 64K tokenów i jest przeznaczony szczególnie do zadań agentowych, kodowania, długiego kontekstu i wielomodalnego rozumienia.","Oficjalna karta modelu zawiera wyniki oceny bezpieczeństwa i frontiera; Google podaje, że Gemini 3.1 Pro utrzymuje wyniki poniżej progów alertowych w ocenach FSF dla CBRN, cyber, harmful manipulation, machine learning R&D i misalignment, a także że testy bezpieczeństwa były zgodne z pierwotną oceną Gemini 3 Pro.","nie","Gemini 3.1 Pro to najnowsza iteracja modelu Gemini 3 firmy Google, zaprojektowana do złożonych zadań wielomodalnych, w tym obsługi audio, obrazu, wideo i kodu, z oknem kontekstu do 1M tokenów.",[1054,1055,1056,1021,1022,1023,1024,1025,1026,1027],"web","cloud","api",[1058,1059,1060,1061,1062,1063,1064,1065,764,1066],"programiści","zespoły enterprise","badacze","użytkownicy potrzebujący długiego kontekstu i multimodalności","Researchers","Developers","Content creators","Data analysts","Product teams",[1068,1069,1070,1071,1072,1073,1074,1075,1076,1077,1078,1079,1080],"analiza audio","rozumienie multimodalne","agentic workflows","zaawansowane kodowanie","praca na długich dokumentach i repozytoriach kodu","planowanie strategiczne krok po kroku","analiza złożonych danych","Advanced coding and software development","Long-form content summarization","Multimodal data analysis","Agentic workflows and automation","Scientific research and data interpretation","Creative content generation (audio, video, images)","Google DeepMind","2026-08-12T18:20:22.773Z",[1038,1084,1085,1086],"Gemini 3.5 Flash","Claude Opus 4.6","GPT-5.2",{"checked_at":1082,"ready":429,"failed":1088},[],"Jest to model AI dostępny przez API, nie narzędzie do generowania treści","```markdown\n# **Gemini 3.1 Pro – zaawansowany model AI do zadań multimodalnych i agentowych**\n\n**Gemini 3.1 Pro** to flagowy model językowy od Google, zaprojektowany z myślą o zaawansowanych zadaniach **wielomodalnych** (tekst, audio, obrazy, wideo, kod) oraz **agentowych workflows**. Narzędzie skierowane jest do deweloperów, przedsiębiorstw i badaczy, którzy potrzebują skalowalnych rozwiązań AI o **rekordowym oknie kontekstu (do 1M tokenów)** i wysokiej precyzji rozumowania.\n\n---\n\n## **Najważniejsze funkcje**\n\n1. **Natywnie multimodalny model**\n   - Przetwarza i generuje odpowiedzi na podstawie **tekstu, audio, obrazów, wideo** oraz **całych repozytoriów kodu** w jednym workflow, eliminując potrzebę integracji wielu specjalistycznych modeli.\n   - Przykłady: analiza nagrań wideo z transkrypcją, generowanie kodu na podstawie screenshotów interfejsów czy odpowiadanie na pytania o treści multimedialne.\n\n2. **Ogromne okno kontekstu (do 1M tokenów)**\n   - Pozwala na przetwarzanie **długich dokumentów, książek, baz kodu lub historii czatu** bez utraty spójności.\n   - Wyjście tekstowe ograniczone do **64K tokenów**, co wystarcza na generowanie rozbudowanych raportów czy fragmentów kodu.\n\n3. **Zaawansowane rozumowanie i kodowanie**\n   - Radzi sobie z **złożonymi zadaniami logicznymi**, w tym matematyką, analizą danych czy debugowaniem kodu w wielu językach programowania.\n   - Wspiera **agentowe workflows** – może autonomicznie planować i wykonywać sekwencje zadań (np. pobieranie danych → analiza → generowanie wizualizacji).\n\n4. **Optymalizacja pod kątem API**\n   - Został zaprojektowany z myślą o **integracji z systemami przedsiębiorstw** (np. przez Vertex AI lub bezpośrednie API).\n   - Możliwe dostosowanie do specyficznych przypadków użycia (fine-tuning).\n\n5. **Wysoka wydajność w benchmarkach**\n   - Według danych Google, model osiąga **lepsze wyniki niż konkurencja** (np. GPT-4) w testach rozumienia multimodalnego i kodowania.\n\n---\n\n## **Dla kogo jest Gemini 3.1 Pro?**\n\n- **Deweloperzy i inżynierowie AI** – do budowy zaawansowanych aplikacji (np. chatboty z obsługą multimediów, narzędzia do analizy kodu).\n- **Przedsiębiorstwa i startupy** – do automatyzacji procesów (np. przetwarzanie dokumentacji, analiza danych z wielu źródeł).\n- **Badacze i naukowcy** – do eksperymentów z modelami wielomodalnymi, np. w dziedzinie **computer vision** czy **NLP**.\n- **Tworcy treści i designerzy** – do generowania opisu obrazów, edycji wideo czy tworzenia interaktywnych prezentacji.\n- **Firmy z sektora finansowego\u002Fmedycznego** – do analizy złożonych danych (np. skany dokumentów + nagrania konsultacji).\n\n---\n\n## **Cena**\n\nOficjalne informacje o **cenach Gemini 3.1 Pro** nie zostały jeszcze opublikowane przez Google (stan na czerwiec 2024). Wiadomo, że:\n- Model jest **płatny** (brak darmowego planu dla wersji Pro).\n- Dostęp odbywa się głównie przez **Google Cloud Vertex AI** lub bezpośrednie API, gdzie koszt zależy od:\n  - Liczby tokenów (wejściowych\u002Fwyjściowych).\n  - Rodzaju zadań (np. przetwarzanie multimediów może być droższe niż czysty tekst).\n- W przypadku wcześniejszych wersji Gemini (np. 1.5 Pro) stawki API rozpoczynały się od **~$0.0005 za 1K tokenów** (input) i **~$0.0015 za 1K tokenów** (output), ale **dla 3.1 Pro mogą być inne**.\n\n**Rekomendacja:** Sprawdź [oficjalną dokumentację Google Cloud](https:\u002F\u002Fcloud.google.com\u002Fvertex-ai) lub skontaktuj się z zespołem sprzedaży Google w celu uzyskania aktualnych wycen.\n\n---\n\n## **Zalety i wady**\n\n### ✅ **Zalety**\n1. **Unikalna multimodalność** – jeden model obsługuje tekst, audio, wideo i kod, co upraszcza architekturę systemów AI.\n2. **Rekordowe okno kontekstu** (1M tokenów) – idealne do analizy długich dokumentów lub historii czatu.\n3. **Silne rozumowanie i kodowanie** – lepsze wyniki w benchmarkach niż wiele konkurencyjnych modeli (np. w zadanich matematycznych czy generowaniu kodu).\n\n### ❌ **Wady**\n1. **Brak transparentnych cen** – Google nie udostępniło jeszcze jasnych tabel cenowych dla 3.1 Pro, co utrudnia planowanie budżetu.\n2. **Ograniczone dostępność** – model może być początkowo dostępny tylko dla wybranych partnerów lub w ramach programu wczesnego dostępu.\n3. **Wysokie koszty dla dużych obciążeń** – przy przetwarzaniu milionów tokenów (np. wideo HD) koszty mogą szybko rosnąć.\n\n---\n\n## **Werdykt**\n\n**Gemini 3.1 Pro to obecnie jeden z najbardziej zaawansowanych modeli AI na rynku**, szczególnie dla zadań wymagających **multimodalności, długiego kontekstu i agentowego rozumowania**. Jeśli potrzebujesz narzędzia do **złożonych analiz, automatyzacji procesów biznesowych lub budowy innowacyjnych aplikacji**, warto śledzić jego rozwój i testować w ramach dostępnych programów. **Brak jasnych cen i ograniczonej dostępności** mogą jednak stanowić barierę dla mniejszych zespołów – w takim przypadku warto rozważyć tańsze alternatywy (np. Gemini 1.",{"source_page_url":799,"final_url":799,"fetched_at":874},{"alternatives_count":184,"active_deals":184},[],[],{"data":1096},[1097,1107,1113,1121,1129,1136,1143,1150,1158,1165,1172,1179,1186,1194,1201,1207,1214,1222,1230,1238,1245,1252,1260,1267,1275,1282,1288,1295,1302,1309,1316,1323,1330,1336,1343,1350,1357,1363,1368,1373,1378,1382,1387,1392,1397,1401,1406,1410,1415,1419,1423,1427,1431,1435,1439,1442,1446,1450,1454,1457,1461,1464,1467,1471,1474,1477,1480,1483,1486,1489,1492,1496,1499,1502,1505,1509,1518,1523,1526,1531,1539,1543,1547,1551,1555,1563,1571,1575,1579,1583,1587,1590,1597,1604,1612,1620,1624,1628,1636,1640],{"id":1098,"type":1099,"title":1100,"body":1101,"tool_name":1102,"z2u_price":1103,"z2u_url":1104,"status":1105,"read_at":48,"created_at":1106},1348,"price_drop","💰 Vora — spadek ceny","Platforma: Dealify | Kategoria: Sales & Marketing | Cena: €67.95 (regular: €670.95) | Rabat: 90%","Vora",67.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fvora","unread","2026-10-23T04:15:26.716Z",{"id":1108,"type":1099,"title":1109,"body":1101,"tool_name":1110,"z2u_price":1103,"z2u_url":1111,"status":1105,"read_at":48,"created_at":1112},1347,"💰 Seamless QR Code — spadek ceny","Seamless QR Code","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fseamless-qr-code","2026-10-23T04:15:26.351Z",{"id":1114,"type":1099,"title":1115,"body":1116,"tool_name":1117,"z2u_price":1118,"z2u_url":1119,"status":1105,"read_at":48,"created_at":1120},1346,"💰 Inspire Sales Academy — spadek ceny","Platforma: Dealify | Kategoria: Sales & Marketing | Cena: €226.95 (regular: €566.95) | Rabat: 60%","Inspire Sales Academy",226.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Finspire-sales-academy","2026-10-23T04:15:25.498Z",{"id":1122,"type":1099,"title":1123,"body":1124,"tool_name":1125,"z2u_price":1126,"z2u_url":1127,"status":1105,"read_at":48,"created_at":1128},1345,"💰 Contact Concert — spadek ceny","Platforma: Dealify | Kategoria: Sales & Marketing | Cena: €101.95 (regular: €1010.95) | Rabat: 90%","Contact Concert",101.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fcontact-concert","2026-10-23T04:15:24.932Z",{"id":1130,"type":1099,"title":1131,"body":1132,"tool_name":1133,"z2u_price":1103,"z2u_url":1134,"status":1105,"read_at":48,"created_at":1135},1344,"💰 OnChat — spadek ceny","Platforma: Dealify | Kategoria: E-commerce | Cena: €67.95 (regular: €670.95) | Rabat: 90%","OnChat","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fonchat","2026-10-23T04:15:23.130Z",{"id":1137,"type":1099,"title":1138,"body":1139,"tool_name":1140,"z2u_price":1103,"z2u_url":1141,"status":1105,"read_at":48,"created_at":1142},1343,"💰 Helpmate — spadek ceny","Platforma: Dealify | Kategoria: E-commerce | Cena: €67.95 (regular: €101.95) | Rabat: 33%","Helpmate","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fhelpmate","2026-10-23T04:15:22.879Z",{"id":1144,"type":1099,"title":1145,"body":1146,"tool_name":1147,"z2u_price":1103,"z2u_url":1148,"status":1105,"read_at":48,"created_at":1149},1342,"💰 Playix.cloud — spadek ceny","Platforma: Dealify | Kategoria: Cloud | Cena: €67.95 (regular: €670.95) | Rabat: 90%","Playix.cloud","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fplayix-cloud","2026-10-23T04:15:21.439Z",{"id":1151,"type":1099,"title":1152,"body":1153,"tool_name":1154,"z2u_price":1155,"z2u_url":1156,"status":1105,"read_at":48,"created_at":1157},1341,"💰 FileLu — spadek ceny","Platforma: Dealify | Kategoria: Cloud | Cena: €169.95 (regular: €397.95) | Rabat: 57%","FileLu",169.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Ffilelu","2026-10-23T04:15:21.280Z",{"id":1159,"type":1099,"title":1160,"body":1161,"tool_name":1162,"z2u_price":1103,"z2u_url":1163,"status":1105,"read_at":48,"created_at":1164},1340,"💰 Purple Photo — spadek ceny","Platforma: Dealify | Kategoria: Creative | Cena: €67.95 (regular: €89.95) | Rabat: 24%","Purple Photo","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fpurple-photo","2026-10-23T04:15:19.971Z",{"id":1166,"type":1099,"title":1167,"body":1168,"tool_name":1169,"z2u_price":1103,"z2u_url":1170,"status":1105,"read_at":48,"created_at":1171},1339,"💰 CursorClip — spadek ceny","Platforma: Dealify | Kategoria: Creative | Cena: €67.95 (regular: €110.95) | Rabat: 39%","CursorClip","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fcursorclip","2026-10-23T04:15:19.613Z",{"id":1173,"type":1099,"title":1174,"body":1175,"tool_name":1176,"z2u_price":1103,"z2u_url":1177,"status":1105,"read_at":48,"created_at":1178},1338,"💰 Creatiyo — spadek ceny","Platforma: Dealify | Kategoria: Creative | Cena: €67.95 (regular: €226.95) | Rabat: 70%","Creatiyo","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fcreatiyo","2026-10-23T04:15:19.453Z",{"id":1180,"type":1099,"title":1181,"body":1182,"tool_name":1183,"z2u_price":1103,"z2u_url":1184,"status":1105,"read_at":48,"created_at":1185},1337,"💰 Blupry Bundle — spadek ceny","Platforma: Dealify | Kategoria: Creative | Cena: €67.95 (regular: €257.95) | Rabat: 74%","Blupry Bundle","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fblupry","2026-10-23T04:15:19.294Z",{"id":1187,"type":1099,"title":1188,"body":1189,"tool_name":1190,"z2u_price":1191,"z2u_url":1192,"status":1105,"read_at":48,"created_at":1193},1336,"💰 Al Webcam Effects — spadek ceny","Platforma: Dealify | Kategoria: Creative | Cena: €135.95 (regular: €1134.95) | Rabat: 88%","Al Webcam 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90%",66.95,"2026-10-15T04:15:25.768Z",{"id":1524,"type":1099,"title":1109,"body":1520,"tool_name":1110,"z2u_price":1521,"z2u_url":1111,"status":1105,"read_at":48,"created_at":1525},1271,"2026-10-15T04:15:25.363Z",{"id":1527,"type":1099,"title":1115,"body":1528,"tool_name":1117,"z2u_price":1529,"z2u_url":1119,"status":1105,"read_at":48,"created_at":1530},1270,"Platforma: Dealify | Kategoria: Sales & Marketing | Cena: €225.95 (regular: €566.95) | Rabat: 60%",225.95,"2026-10-15T04:15:24.523Z",{"id":1532,"type":1099,"title":1533,"body":1534,"tool_name":1535,"z2u_price":1536,"z2u_url":1537,"status":1105,"read_at":48,"created_at":1538},1269,"💰 Glanc AI — spadek ceny","Platforma: Dealify | Kategoria: Sales & Marketing | Cena: €274.95 (regular: €782.95) | Rabat: 65%","Glanc AI",274.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fglanc-ai","2026-10-15T04:15:24.274Z",{"id":1540,"type":1099,"title":1131,"body":1541,"tool_name":1133,"z2u_price":1521,"z2u_url":1134,"status":1105,"read_at":48,"created_at":1542},1268,"Platforma: Dealify | Kategoria: E-commerce | Cena: €66.95 (regular: €669.95) | Rabat: 90%","2026-10-15T04:15:22.062Z",{"id":1544,"type":1099,"title":1138,"body":1545,"tool_name":1140,"z2u_price":1521,"z2u_url":1141,"status":1105,"read_at":48,"created_at":1546},1267,"Platforma: Dealify | Kategoria: E-commerce | Cena: €66.95 (regular: €101.95) | Rabat: 34%","2026-10-15T04:15:21.821Z",{"id":1548,"type":1099,"title":1145,"body":1549,"tool_name":1147,"z2u_price":1521,"z2u_url":1148,"status":1105,"read_at":48,"created_at":1550},1266,"Platforma: Dealify | Kategoria: Cloud | Cena: €66.95 (regular: €669.95) | Rabat: 90%","2026-10-15T04:15:20.429Z",{"id":1552,"type":1099,"title":1160,"body":1553,"tool_name":1162,"z2u_price":1521,"z2u_url":1163,"status":1105,"read_at":48,"created_at":1554},1265,"Platforma: Dealify | Kategoria: Creative | Cena: €66.95 (regular: €89.95) | Rabat: 26%","2026-10-15T04:15:19.024Z",{"id":1556,"type":1099,"title":1557,"body":1558,"tool_name":1559,"z2u_price":1560,"z2u_url":1561,"status":1105,"read_at":48,"created_at":1562},1264,"💰 MiPLR — spadek ceny","Platforma: Dealify | Kategoria: Creative | Cena: €32.95 (regular: €89.95) | Rabat: 63%","MiPLR",32.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fmiplr","2026-10-15T04:15:18.871Z",{"id":1564,"type":1511,"title":1565,"body":1566,"tool_name":1567,"z2u_price":1568,"z2u_url":1569,"status":1105,"read_at":48,"created_at":1570},1263,"🆕 KDPage — €78.95 (-65%)","Platforma: Dealify | Kategoria: Creative | Cena: €78.95 (regular: €225.95) | Rabat: 65%","KDPage",78.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fkdpage","2026-10-15T04:15:18.725Z",{"id":1572,"type":1099,"title":1167,"body":1573,"tool_name":1169,"z2u_price":1521,"z2u_url":1170,"status":1105,"read_at":48,"created_at":1574},1262,"Platforma: Dealify | Kategoria: Creative | Cena: €66.95 (regular: €110.95) | Rabat: 40%","2026-10-15T04:15:18.572Z",{"id":1576,"type":1099,"title":1174,"body":1577,"tool_name":1176,"z2u_price":1521,"z2u_url":1177,"status":1105,"read_at":48,"created_at":1578},1261,"Platforma: Dealify | Kategoria: Creative | Cena: €66.95 (regular: €225.95) | Rabat: 70%","2026-10-15T04:15:18.431Z",{"id":1580,"type":1099,"title":1181,"body":1581,"tool_name":1183,"z2u_price":1521,"z2u_url":1184,"status":1105,"read_at":48,"created_at":1582},1260,"Platforma: Dealify | Kategoria: Creative | Cena: €66.95 (regular: €257.95) | Rabat: 74%","2026-10-15T04:15:18.289Z",{"id":1584,"type":1099,"title":1196,"body":1585,"tool_name":1198,"z2u_price":1521,"z2u_url":1199,"status":1105,"read_at":48,"created_at":1586},1259,"Platforma: Dealify | Kategoria: Productivity | Cena: €66.95 (regular: €669.95) | Rabat: 90%","2026-10-15T04:15:16.690Z",{"id":1588,"type":1099,"title":1203,"body":1585,"tool_name":1204,"z2u_price":1521,"z2u_url":1205,"status":1105,"read_at":48,"created_at":1589},1258,"2026-10-15T04:15:16.547Z",{"id":1591,"type":1099,"title":1592,"body":1593,"tool_name":1594,"z2u_price":1560,"z2u_url":1595,"status":1105,"read_at":48,"created_at":1596},1257,"💰 StickyPassword — spadek ceny","Platforma: Dealify | Kategoria: Productivity | Cena: €32.95 (regular: €218.95) | Rabat: 85%","StickyPassword","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fstickypassword","2026-10-15T04:15:15.904Z",{"id":1598,"type":1099,"title":1599,"body":1600,"tool_name":1601,"z2u_price":1560,"z2u_url":1602,"status":1105,"read_at":48,"created_at":1603},1256,"💰 Notepad — spadek ceny","Platforma: Dealify | Kategoria: Productivity | Cena: €32.95 (regular: €329.95) | Rabat: 90%","Notepad","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fnotepad","2026-10-15T04:15:15.108Z",{"id":1605,"type":1099,"title":1606,"body":1607,"tool_name":1608,"z2u_price":1609,"z2u_url":1610,"status":1105,"read_at":48,"created_at":1611},1255,"💰 FlashBooks — spadek ceny","Platforma: Dealify | Kategoria: Productivity | Cena: €282.95 (regular: €1973.95) | Rabat: 86%","FlashBooks",282.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fflashbooks","2026-10-15T04:15:14.664Z",{"id":1613,"type":1099,"title":1614,"body":1615,"tool_name":1616,"z2u_price":1617,"z2u_url":1618,"status":1105,"read_at":48,"created_at":1619},1254,"💰 Envoice — spadek ceny","Platforma: Dealify | Kategoria: Business | Cena: €157.95 (regular: €1576.95) | Rabat: 90%","Envoice",157.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fenvoice","2026-10-15T04:15:12.373Z",{"id":1621,"type":1099,"title":1247,"body":1622,"tool_name":1249,"z2u_price":1521,"z2u_url":1250,"status":1105,"read_at":48,"created_at":1623},1253,"Platforma: Dealify | Kategoria: Business | Cena: €66.95 (regular: €669.95) | Rabat: 90%","2026-10-15T04:15:12.225Z",{"id":1625,"type":1099,"title":1254,"body":1626,"tool_name":1256,"z2u_price":1404,"z2u_url":1258,"status":1105,"read_at":48,"created_at":1627},1252,"Platforma: Dealify | Kategoria: Business | Cena: €336.95 (regular: €3369.95) | Rabat: 90%","2026-10-15T04:15:11.677Z",{"id":1629,"type":1099,"title":1630,"body":1631,"tool_name":1632,"z2u_price":1633,"z2u_url":1634,"status":1105,"read_at":48,"created_at":1635},1251,"💰 LeadLocator — spadek ceny","Platforma: Dealify | Kategoria: Business | Cena: €191.95 (regular: €1917.95) | Rabat: 90%","LeadLocator",191.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fleadlocator","2026-10-15T04:15:11.332Z",{"id":1637,"type":1099,"title":1277,"body":1638,"tool_name":1279,"z2u_price":1521,"z2u_url":1280,"status":1105,"read_at":48,"created_at":1639},1250,"Platforma: Dealify | Kategoria: Social Media | Cena: €66.95 (regular: €669.95) | Rabat: 90%","2026-10-15T04:15:09.802Z",{"id":1641,"type":1099,"title":1284,"body":1638,"tool_name":1285,"z2u_price":1521,"z2u_url":1286,"status":1105,"read_at":48,"created_at":1642},1249,"2026-10-15T04:15:09.556Z",{"data":1644},{"left":305,"right":800,"confidence":1645,"content":1646,"generatedAt":1672,"model":1673},0.68,{"h1":1647,"lead":1648,"sections":1649,"verdict":1662,"faq":1665},"Inkling vs Gemini 3.1 Pro — otwarte audio vs multimodalny gigant","Inkling to otwarty model 975B parametrów z obsługą tekstu, obrazu i audio oraz 1M tokenów kontekstu, dostępny za darmo na licencji Apache 2.0. Gemini 3.1 Pro to flagowy model Google’a, który obsługuje dodatkowo wideo i kod, ale jego cena i dostępność do samodzielnego hostingu nie są znane. Porównujemy je pod kątem funkcji, kosztów i elastyczności wdrożenia.",[1650,1653,1656,1659],{"h2":1651,"body":1652},"Czym się różnią pod względem otwartości i licencji?","Inkling jest w pełni otwarty – wagi modelu udostępniono na licencji Apache 2.0, co pozwala na dowolne użycie, modyfikacje i samodzielne hostowanie. Gemini 3.1 Pro to zamknięty model Google’a, dostępny przez API, aplikację Gemini, Google AI Studio i Vertex AI. Możliwość samodzielnego uruchomienia Gemini nie jest potwierdzona.",{"h2":1654,"body":1655},"Jakie dane wejściowe obsługują oba modele?","Inkling przyjmuje tekst, obrazy i audio. Gemini 3.1 Pro rozszerza to o wideo, kod i repozytoria kodu. Oba modele mają 1-milionowy kontekst tokenów, co pozwala na przetwarzanie długich dokumentów, nagrań czy rozmów.",{"h2":1657,"body":1658},"Ile kosztuje korzystanie z Inkling i Gemini 3.1 Pro?","Inkling jest darmowy przy samodzielnym hostowaniu. Przy inferencji przez partnerów API (np. Together AI, Baseten) kosztuje 4,05 USD za 1000 tokenów. Cena Gemini 3.1 Pro nie jest znana – Google nie podał jeszcze stawek ani informacji o wersji próbnej. To utrudnia bezpośrednie porównanie finansowe.",{"h2":1660,"body":1661},"Który model oferuje więcej funkcji technicznych?","Inkling posiada architekturę Mixture of Experts (MoE), regulowany wysiłek wnioskowania (fine-tuning głębokości myślenia), wsparcie dla wywoływania narzędzi, strukturalne wyjście oraz wiele formatów kwantyzacji (GGUF, BF16, NVFP4). Gemini 3.1 Pro wyróżnia się zaawansowanym kodowaniem, działaniem agentowym, wielojęzycznością i wysoką wydajnością w rozumowaniu. Nie ma jednak informacji o kwantyzacji czy możliwości dostrajania głębokości myślenia.",{"pick_x":1663,"pick_y":1664},"Inkling wybierzesz, jeśli potrzebujesz otwartego modelu audio\u002Ftekst\u002Fobraz na własny serwer, chcesz go dostrajać (Tinker) i kontrolować koszty – zwłaszcza przy dużych obciążeniach, gdzie stawka 4,05 USD\u002F1k tokenów może być akceptowalna. Dla zespołów ceniących wolność licencyjną i możliwość samodzielnej optymalizacji.","Gemini 3.1 Pro wybierzesz, jeśli potrzebujesz wsparcia dla wideo, zaawansowanego kodowania i działania agentowego w ekosystemie Google’a. Jeśli nie zależy ci na otwartości, a na gotowej, potężnej multimodalności – i jeśli cena nie jest jeszcze znana, ale zakładasz, że Google będzie konkurencyjny.",[1666,1669],{"q":1667,"a":1668},"Czy oba modele obsługują audio w czasie rzeczywistym?","Inkling przyjmuje audio jako jeden z typów wejścia, ale nie określono opóźnień. Gemini 3.1 Pro również obsługuje audio, ale szczegóły implementacji (np. streaming) nie są ujawnione. W praktyce zależy to od API i konfiguracji.",{"q":1670,"a":1671},"Który model ma większą społeczność i wsparcie?","Inkling ma otwarte wagi i jest rozwijany przez Thinking Machines Lab – społeczność open-source może tworzyć wokół niego narzędzia. Gemini 3.1 Pro to produkt Google’a z profesjonalnym wsparciem w Vertex AI, ale bez możliwości modyfikacji modelu.","2026-08-10T23:10:11.029Z","deepseek\u002Fdeepseek-v4-flash"]