[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$ffFUljuElgdP-i1mZ4wzexYr4TTvUKDDrmCNWTaL4Fwo":3,"$f-gwIXJgDGgGv9bmRu29vUWmMAqDVQVCWspFuwnTv5fs":28,"$fs8386zdAUI-Rj5TzVbzTerjIHduHTXuIcIJmyYrxqs0":127,"$fBuPzgYSZ2qvCIvMieT5y4oi-25q1ef7qJGzWFioAT14":300,"$fDWP5bqQ2NOHLsnSj7U8CW9VfI4M5kNu3j1Ge6kFrI_c":797,"notifications-unread":1001,"$f8_PQ6D-L7Wn_554fpNPuZMOvDGXdUbwnA1IKVf-9738":1549},[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":998,"alternatives":999,"deals":1000},{"source":799,"source_key":800,"source_page_url":801,"name":802,"slug":803,"description":804,"title":805,"category":806,"access_model":807,"pricing_evidence":808,"capability_evidence":817,"headings":821,"breadcrumbs":834,"pros":835,"cons":844,"external_links":850,"affiliate_links":899,"official_link_candidates":903,"text_excerpt":906,"fetched_at":907,"final_url":801,"http_status":425,"content_type":908,"content_hash":909,"cache_file":910,"published":429,"verification_status":430,"official_url":911,"api_available":675,"blog_titles":912,"data_region":913,"data_training":914,"entity_type":915,"external_rating":916,"features":917,"free_summary":929,"gdpr":930,"integrations":931,"languages":939,"license":942,"limitations":943,"model_names":947,"polish_support":734,"pricing_plans":948,"pricing_summary":951,"privacy_summary":952,"release_date":680,"research_links":953,"research_summary":954,"security_summary":955,"self_hosted":956,"short_description":804,"starting_price":680,"supported_platforms":957,"target_users":959,"trial_summary":929,"use_cases":971,"vendor":802,"status":782,"last_verified_at":983,"alternatives":984,"quality":993,"category_v3":150,"category_v3_main":129,"category_v3_label":151,"category_v3_reason":995,"deep_card":996,"deep_card_generated":429,"category_v3_main_label":130,"_category_fixed":429,"evidence":997},"Futurepedia","futurepedia","https:\u002F\u002Fwww.futurepedia.io\u002Ftool\u002Ftrint","Trint","trint","Transform audio\u002Fvideo to editable, searchable text; supports 40+ languages.","Trint AI Reviews: Use Cases, Pricing & Alternatives","transcriber","free_trial",[809,810,811,812,813,814,815,816],"Trint AI Reviews: Use Cases, Pricing & Alternatives Home AI Tools Newsletter Resources Free Claude Course Newsletter Archive AI Agents AI Innovations AI Tutorials AI Automations 101 Login Join For Free Home > AI Tool > Trint Trint","Pricing Model: Free Trial Follow: twitter youtube facebook linkedin instagram 10 Visit Site What is Trint Trint Reviews Trint Alternatives Updated 08\u002F04\u002F2026 Featured AI Tools","Unlock AI Mastery Instant Access to 20+ Premium AI Courses 500+ In-Depth Training Videos 5500+ Expert-Crafted Prompts New Content Added Weekly Taught by leading AI educator Saj Adib Start Learning For Free Did you find this content helpful?","Advertiser Disclosure: Futurepedia.io is committed to rigorous editorial standards to provide our users with accurate and helpful content. To keep our site free, we may receive compensation when you click some links on our site.","Free Claude Course Best AI Art Generators Best AI Image Generators Best AI Chatbots Best AI Text Generators Best AI 3D Generators All Resources Skill Leap Company","About Us Contact Us Submit a Tool Update a Tool Login Editorial Guidelines Sign up for free Linkedin Twitter YouTube TikTok Instagram Facebook © 2026 Futurepedia LLC. All rights reserved.","Cost Concerns: While offering powerful features, the pricing might be a consideration for freelancers or smaller organizations.","Free Trial: Experience Trint with a free trial to explore its features before committing to a subscription. Subscription Plans: Offers various tiers, tailored to different user needs and organizational sizes. Disclaimer: Please visit the Trint website for the most accurate and current pricing details.",[818,819,820],"Privacy Policy HubSpot Website Terms of Use What is Trint?","Content Management Systems: Easily integrates with various CMS to streamline publishing workflows. Video Editing Software: Compatible with major video editing platforms, enhancing post-production processes. API Access: Offers API for custom integrations, allowing businesses to tailor Trint to their specific needs. Secure Cloud Storage: Integrates with secure cloud services to store and manage transcripts safely. Trint Tutorials:","Accuracy and Reliability: 4.8\u002F5 Ease of Use: 4.6\u002F5 Functionality and Features: 4.7\u002F5 Performance and Speed: 4.5\u002F5 Customization and Flexibility: 4.3\u002F5 Data Privacy and Security: 4.9\u002F5 Support and Resources: 4.4\u002F5 Cost-Efficiency: 4.2\u002F5 Integration Capabilities: 4.5\u002F5 Overall Score: 4.5\u002F5 Summary:",[822,823,824,825,826,827,828,829,830,831,832,833],"Promote Trint","What is Trint?","Key Features:","Pros","Cons","Who is Using Trint?","Pricing:","What Makes Trint Unique?","Compatibilities and Integrations:","Trint Tutorials:","How We Rated It:","Summary:",[],[836,837,838,839,840,841,842,843],"Enhanced accessibility through captioning and translation","Saves time on transcription and content production","User-friendly interface","Suitable for newsrooms, podcasts, financial services, and academic research","Enhanced Accessibility: Provides features like closed captioning and AI-powered translation into 50+ languages, making content accessible to a global audience.","Time-Saving: Significantly reduces the time required for transcription and content production.","User-Friendly Interface: Simplifies the transcription process with an intuitive interface that allows easy navigation and operation.","Versatile Application: Suitable for a wide range of professional uses, from newsrooms and podcast production to financial services and academic research.",[845,846,847,815,848,849],"Pricing may be a consideration for freelancers or smaller organizations","Some users may experience a learning curve with advanced features","Requires a stable internet connection for cloud-based features and collaboration","Complex Features: Some users may experience a learning curve when first utilizing advanced features.","Internet Dependency: Requires a stable internet connection to access cloud-based features and collaborations.",[851,854,857,860,863,866,869,872,875,878,881,884,887,890,893,896],{"url":852,"text":853},"https:\u002F\u002Ftwitter.com\u002Ftrinthq","twitter",{"url":855,"text":856},"https:\u002F\u002Fwww.youtube.com\u002Fc\u002FTrintLtd","youtube",{"url":858,"text":859},"https:\u002F\u002Fwww.facebook.com\u002Ftrint.ltd\u002F","facebook",{"url":861,"text":862},"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Ftrint\u002F","linkedin",{"url":864,"text":865},"https:\u002F\u002Fwww.instagram.com\u002Ftrint_hq\u002F","instagram",{"url":867,"text":868},"https:\u002F\u002Ftrint.com\u002F?utm_source=futurepedia&utm_medium=marketplace&utm_campaign=futurepedia","Visit Site",{"url":870,"text":871},"https:\u002F\u002Fskillleap.futurepedia.io\u002F?utm_source=futurepedia.io&utm_medium=toolpage_banner&utm_campaign=skillleap_home","Start Learning For Free",{"url":873,"text":874},"https:\u002F\u002Fskillleap.futurepedia.io\u002F?utm_source=futurepedia.io&utm_medium=footer&utm_campaign=skillleap_link","Skill Leap",{"url":876,"text":877},"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Ffuturepedia","Linkedin",{"url":879,"text":880},"https:\u002F\u002Fx.com\u002Ffuturepedia_io","Twitter",{"url":882,"text":883},"https:\u002F\u002Fwww.youtube.com\u002F@futurepedia_io","YouTube",{"url":885,"text":886},"https:\u002F\u002Fwww.tiktok.com\u002F@futurepediaio","TikTok",{"url":888,"text":889},"https:\u002F\u002Fwww.instagram.com\u002Ffuturepedia_io\u002F","Instagram",{"url":891,"text":892},"https:\u002F\u002Fwww.facebook.com\u002Ffuturepediadotio\u002F","Facebook",{"url":894,"text":895},"https:\u002F\u002Flegal.hubspot.com\u002Fprivacy-policy","Privacy Policy",{"url":897,"text":898},"https:\u002F\u002Flegal.hubspot.com\u002Fwebsite-terms-of-use","HubSpot Website Terms of Use",[900,901,902],{"url":867,"text":868},{"url":870,"text":871},{"url":873,"text":874},[904,905],{"url":894,"text":895},{"url":897,"text":898},"Trint AI Reviews: Use Cases, Pricing & Alternatives Home AI Tools Newsletter Resources Free Claude Course Newsletter Archive AI Agents AI Innovations AI Tutorials AI Automations 101 Login Join For Free Home > AI Tool > Trint Trint\nRated 0 out of 5 (0) Verified Transform audio\u002Fvideo to editable, searchable text; supports 40+ languages.\nAI Categories: transcriber , legal , research \nPricing Model: Free Trial Follow: twitter youtube facebook linkedin instagram 10 Visit Site What is Trint Trint Reviews Trint Alternatives Updated 08\u002F04\u002F2026 Featured AI Tools\nUnlock AI Mastery Instant Access to 20+ Premium AI Courses 500+ In-Depth Training Videos 5500+ Expert-Crafted Prompts New Content Added Weekly Taught by leading AI educator Saj Adib Start Learning For Free Did you find this content helpful?\nRate as helpful Rate as not helpful Promote Trint\nFeatured on Futurepedia 10 Copy Embed Code Write a review Related Categories\nTrint alternatives\nAdvertiser Disclosure: Futurepedia.io is committed to rigorous editorial standards to provide our users with accurate and helpful content. To keep our site free, we may receive compensation when you click some links on our site.\nCategories\nProductivity Tools Image Generators Text Generators Video Tools Art Generators Audio Generators All AI Tools Resources\nFree Claude Course Best AI Art Generators Best AI Image Generators Best AI Chatbots Best AI Text Generators Best AI 3D Generators All Resources Skill Leap Company\nAbout Us Contact Us Submit a Tool Update a Tool Login Editorial Guidelines Sign up for free Linkedin Twitter YouTube TikTok Instagram Facebook © 2026 Futurepedia LLC. All rights reserved.\nPrivacy Policy HubSpot Website Terms of Use What is Trint?\nTrint is a cutting-edge AI tool designed to transform audio and video content into editable, searchable text. This platform is a boon for professionals across various industries, including journalism, law, education, and content creation. By leveraging AI technology, Trint simplifies the transcription process, offering features like multi-language support, real-time collaboration, and seamless integration with numerous platforms, thereby streamlining workflows and enhancing productivity.\nKey Features:\nMulti-Language Transcription: Converts spoken language from audio and video files into text in over 40 languages with up to 99% accuracy. Real-Time Collaboration Tools: Allows teams to edit, comment, and highlight transcripts simultaneously, facilitating efficient teamwork. Integration Capabilities: Trint integrates with numerous platforms, including content management systems and video editing software, to streamline user workflows. Advanced Security: Adheres to ISO 27001 certification, ensuring that all data is handled with the highest security standards. Pros\nEnhanced Accessibility: Provides features like closed captioning and AI-powered translation into 50+ languages, making content accessible to a global audience.\nTime-Saving: Significantly reduces the time required for transcription and content production.\nUser-Friendly Interface: Simplifies the transcription process with an intuitive interface that allows easy navigation and operation.\nVersatile Application: Suitable for a wide range of professional uses, from newsrooms and podcast production to financial services and academic research.\nCons\nCost Concerns: While offering powerful features, the pricing might be a consideration for freelancers or smaller organizations.\nComplex Features: Some users may experience a learning curve when first utilizing advanced features.\nInternet Dependency: Requires a stable internet connection to access cloud-based features and collaborations.\nWho is Using Trint?\nNewsrooms: Utilized for quickly transcribing interviews and creating accurate news reports. Law Firms: Employed to transcribe legal proceedings and client consultations efficiently. Educators and Researchers: Helps in transcribing lectures and qualitative research interviews. Podcasters and Content Creators: Used to transcribe audio content for easier editing and production. Uncommon Use Cases: Trint is also used by government bodies like the Nebraska State Legislature to manage and transcribe legislative sessions. Pricing:\nFree Trial: Experience Trint with a free trial to explore its features before committing to a subscription. Subscription Plans: Offers various tiers, tailored to different user needs and organizational sizes. Disclaimer: Please visit the Trint website for the most accurate and current pricing details.\nWhat Makes Trint Unique?\nTrint stands out due to its superior transcription accuracy and multi-language capabilities. Its integration of collaborative tools directly into the transcription process uniquely supports teamwork and productivity, setting it apart from other transcription services.\nCompatibilities and Integrations:\nContent Management Systems: Easily integrates with various CMS to streamline publishing workflows. Video Editing Software: Compatible with major video editing platforms, enhancing post-production processes. API Access: Offers API for custom integrations, allowing businesses to tailor Trint to their specific needs. Secure Cloud Storage: Integrates with secure cloud services to store and manage transcripts safely. Trint Tutorials:\nExplore a range of tutorials and resources on Trint's website, providing guidance from basic setup to advanced features.\nHow We Rated It:\nAccuracy and Reliability: 4.8\u002F5 Ease of Use: 4.6\u002F5 Functionality and Features: 4.7\u002F5 Performance and Speed: 4.5\u002F5 Customization and Flexibility: 4.3\u002F5 Data Privacy and Security: 4.9\u002F5 Support and Resources: 4.4\u002F5 Cost-Efficiency: 4.2\u002F5 Integration Capabilities: 4.5\u002F5 Overall Score: 4.5\u002F5 Summary:\nTrint excels in transforming the transcription landscape, offering robust, accurate, and secure transcription solutions that cater to a diverse range of industries. Its ability to support multiple languages and integrate with various platforms makes it an indispensable tool for professionals aiming to enhance their productivity and content quality.","2026-08-09T00:08:40.882Z","text\u002Fhtml; charset=utf-8","2d1f252461f9be47e74518c60c1e8f571db9675394fa4461dd367f2dd5f4993a","data\u002Fcatalog\u002Fcache\u002Ffuturepedia\u002F87aad3a29e9bc616e266935c33e26019544c65c00386a0005efbe162ede48a72.html","https:\u002F\u002Ftrint.com",[],"EU or US","No — Trint states it never listens to recordings to train its algorithms","application","0\u002F5",[918,919,920,921,922,923,924,925,926,927,928,840,841,842,843],"Transforms audio and video into editable, searchable text","Multi-language transcription in over 40 languages","Real-time collaboration tools for editing, commenting, and highlighting","Integration capabilities with CMS and video editing software","API access for custom integrations","Advanced security with ISO 27001 certification","Closed captioning and AI-powered translation into 50+ languages","Multi-Language Transcription: Converts spoken language from audio and video files into text in over 40 languages with up to 99% accuracy.","Real-Time Collaboration Tools: Allows teams to edit, comment, and highlight transcripts simultaneously, facilitating efficient teamwork.","Integration Capabilities: Trint integrates with numerous platforms, including content management systems and video editing software, to streamline user workflows.","Advanced Security: Adheres to ISO 27001 certification, ensuring that all data is handled with the highest security standards.","Experience Trint with a free trial to explore its features before committing to a subscription.","Yes — Trint states it is fully GDPR compliant",[932,933,934,935,936,937,938],"Content management systems","Video editing software","Secure cloud services","Content Management Systems","Video Editing Software","API Access","Secure Cloud Storage",[940,941],"40+ languages","50+ languages (for translation)","Proprietary",[944,945,946,815,848,849],"Cloud-based service; stable internet connection required","Official pricing details were not available on the cited pages","No evidence found for self-hosted deployment",[],[949],{"plan_name":950,"access_model":807,"price":680,"currency":680,"billing_period":680,"included_usage":680,"trial_days":680,"card_required":680,"source_evidence":929},"Free Trial","Futurepedia lists Trint as Free Trial. The official pricing page linked from Trint was not accessible in the gathered evidence, so exact plan names, prices, currencies, billing periods, and usage limits could not be verified.","Trint says user data is not used to train its algorithms and that recordings are not listened to by the company. Data can be stored in the EU or US, and data is stored by default for the lifetime of the subscription with secure deletion available on request or at contract termination.",[],"Trint is an AI transcription and content workflow platform that converts audio and video into searchable, editable text. The gathered evidence supports strong multilingual transcription, collaboration, API access, GDPR compliance, and EU\u002FUS data residency options, but exact official pricing and several commercial details could not be verified from accessible official pages.","Trint states it is ISO 27001 and Cyber Essentials certified, encrypts data in transit and at rest, uses HTTPS\u002FTLS 1.2 and AES-256, enforces MFA for employees, and processes payments through Stripe. It also states that employees do not look at user data without permission.","No evidence found",[751,958,935,936,937,938],"Cloud",[960,961,962,963,964,965,966,967,968,969,970],"Journalists","Law firms","Educators and researchers","Podcasters and content creators","Government bodies","Enterprise content teams","Newsrooms","Law Firms","Educators and Researchers","Podcasters and Content Creators","Government Bodies (e.g., Nebraska State Legislature)",[972,973,974,975,976,977,978,979,980,981,982],"Transcribing interviews","Creating searchable transcripts from audio and video","Collaborative editing and review of transcripts","Building stories or scripts from transcripts","Captioning and translation for global audiences","Legal and research transcription","Newsrooms: Utilized for quickly transcribing interviews and creating accurate news reports.","Law Firms: Employed to transcribe legal proceedings and client consultations efficiently.","Educators and Researchers: Helps in transcribing lectures and qualitative research interviews.","Podcasters and Content Creators: Used to transcribe audio content for easier editing and production.","Uncommon Use Cases: Trint is also used by government bodies like the Nebraska State Legislature to manage and transcribe legislative sessions.","2026-08-12T18:22:13.915Z",[985,986,987,988,989,990,991,992],"Adobe Podcast","timeOS","Descript","MeetGeek","Easy Peasy AI","Krisp","Rewind","Fireflies.ai",{"checked_at":983,"ready":429,"failed":994},[],"Narzędzie służy do transkrypcji audio\u002Fwideo do tekstu, co jest funkcją z kategorii Audio i mowa.","```markdown\n# **Trint – AI do transkrypcji audio i wideo z edycją w czasie rzeczywistym**\n\n**Krótki lead**\n**Trint** to zaawansowane narzędzie AI, które automatycznie zamienia nagrania audio i wideo na edytowalny, przeszukiwalny tekst – z obsługą ponad **40 języków**, w tym polskiego. To idealne rozwiązanie dla dziennikarzy, badaczy, twórców treści oraz zespołów, które potrzebują szybkiej, dokładnej transkrypcji i współpracy nad tekstem w jednym miejscu.\n\n---\n\n## **Najważniejsze funkcje**\n\n1. **Automatyczna transkrypcja z AI**\n   - Przetwarza pliki audio\u002Fwideo (np. wywiady, podcasty, spotkania) na tekst z wysoką dokładnością, zachowując podział na mówców i znaczniki czasowe.\n   - Obsługuje **ponad 40 języków**, w tym polski, angielski, niemiecki czy hiszpański.\n\n2. **Edycja i współpraca w czasie rzeczywistym**\n   - Tekst transkrypcji jest interaktywny – można go edytować bezpośrednio w przeglądarce, dodawać komentarze, zaznaczać fragmenty i tagować kluczowe momenty.\n   - Funkcje podobne do **Google Docs**, umożliwiające pracę zespołową (np. redakcja wywiadów czy analizy badawcze).\n\n3. **Przeszukiwalne i eksportowalne transkrypcje**\n   - Możliwość wyszukiwania konkretnych słów lub fraz w nagraniu (przyspiesza analizę długich materiałów).\n   - Eksport do formatów takich jak **TXT, DOCX, SRT (napisy), JSON** lub integracja z narzędziami do edycji wideo (np. Adobe Premiere, Final Cut Pro).\n\n4. **Integracje i API**\n   - Łączy się z systemami **CMS** (np. WordPress), platformami wideo (YouTube, Vimeo) oraz narzędziami do analizy danych.\n   - Dostęp do **API** dla firm, które chcą zautomatyzować transkrypcję w swoich aplikacjach.\n\n5. **Bezpieczeństwo i zgodność**\n   - Opcje szyfrowania danych i zgodność z **RODO\u002FGDPR**, co jest kluczowe dla firm i instytucji publicznych.\n\n---\n\n## **Dla kogo jest Trint?**\n\n- **Dziennikarze i media** – transkrypcja wywiadów, materiałów reportażowych lub live’ów z możliwością szybkiego cytowania.\n- **Badacze i naukowcy** – analiza jakościowa nagrań (np. wywiadów badawczych, focus group).\n- **Twórcy treści** (podcasterzy, youtuberzy) – generowanie napisów, skryptów lub artykułów na podstawie nagrań.\n- **Firmy i korporacje** – dokumentowanie spotkań, szkolenia wideo lub archiwizacja wewnętrznych komunikacji.\n- **Prawnicy i służby publiczne** – transkrypcja przesłuchań, rozpraw lub nagrań z monitoringu (z zachowaniem poufności).\n- **Edukacja** – tworzenie materiałów dla studentów z niepełnosprawnościami lub tłumaczenie wykładów.\n\n---\n\n## **Cena**\n\nTrint oferuje **darmowy okres próbny**, który pozwala przetestować wszystkie funkcje przed zakupem subskrypcji. Dokładne ceny i plany nie są publicznie dostępne (brak aktualnych danych na oficjalnej stronie w momencie pisania tego opisu). Według informacji z zewnętrznych źródeł (np. **Futurepedia**), model oparty jest na abonamencie, gdzie koszt zależy od:\n- Liczby godzin transkrypcji miesięcznie,\n- Liczby użytkowników w zespole,\n- Dostępu do zaawansowanych funkcji (np. API, integracje).\n\n**Przykładowe szacunki** (nieoficjalne, na podstawie historycznych danych):\n- Plany indywidualne: od **~$15–$50\u002Fmies.** za kilka godzin transkrypcji.\n- Plany dla zespołów\u002Ffirm: od **~$100\u002Fmies.** wzwyż (z opcjami współpracy i większymi limitami).\n\n*Aby poznać aktualne ceny, należy skontaktować się z zespołem Trint lub odwiedzić [oficjalną stronę](https:\u002F\u002Ftrint.com\u002F).*\n\n---\n\n## **Zalety i wady**\n\n✅ **Zalety:**\n1. **Dokładność i szybkość** – AI radzi sobie z różnymi akcentami, szumami tła i technicznym żargonem (lepsze niż darmowe alternatywy jak Otter.ai w wersji podstawowej).\n2. **Współpraca w czasie rzeczywistym** – unikalna funkcja edycji zespołowej, podobna do Google Docs, ale zintegrowana z nagraniami.\n3. **Wszechstronność** – obsługa wielu języków, formatów eksportu i integracji z narzędziami profesjonalnymi (np. Adobe Suite).\n\n❌ **Wady:**\n1. **Koszt** – dla indywidualnych użytkowników lub małych projektów może być droższy niż konkurencja (np. Descript czy Happy Scribe).\n2. **Ograniczenia darmowego trialu** – brak jasnych informacji o długości okresu próbnego i limitach (np. liczba minut transkrypcji).\n3. **Zależność od jakości nagrania** – przy słabej jakości dźwięku (np. hałas, nakładające się głosy) dokładność może spadać.\n\n---\n\n## **Werdykt**\n\n**Trint to jedno z najbardziej zaawansowanych narzędzi do transkrypcji AI na rynku**, szczególnie dla zespołów, które potrzebują nie tylko automatycznego rozpoznawania mowy, ale także **edycji współdzielonej i integracji z profesjonalnym oprogramowaniem**. Jeśli zależy Ci na precyzji, współpracy i elastyczności eksportu, warto przetestować darmowy trial. Dla użytkowników indywidualnych z prostymi potrzebami (np. sporadyczne transkrypcje) mogą",{"source_page_url":801,"final_url":801,"fetched_at":907},{"alternatives_count":184,"active_deals":184},[],[],{"data":1002},[1003,1013,1019,1027,1035,1042,1049,1056,1064,1071,1078,1085,1092,1100,1107,1113,1120,1128,1136,1144,1151,1158,1166,1173,1181,1188,1194,1201,1208,1215,1222,1229,1236,1242,1249,1256,1263,1269,1274,1279,1284,1288,1293,1298,1303,1307,1312,1316,1321,1325,1329,1333,1337,1341,1345,1348,1352,1356,1360,1363,1367,1370,1373,1377,1380,1383,1386,1389,1392,1395,1398,1402,1405,1408,1411,1415,1424,1429,1432,1437,1445,1449,1453,1457,1461,1469,1477,1481,1485,1489,1493,1496,1503,1510,1518,1526,1530,1534,1542,1546],{"id":1004,"type":1005,"title":1006,"body":1007,"tool_name":1008,"z2u_price":1009,"z2u_url":1010,"status":1011,"read_at":48,"created_at":1012},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":1014,"type":1005,"title":1015,"body":1007,"tool_name":1016,"z2u_price":1009,"z2u_url":1017,"status":1011,"read_at":48,"created_at":1018},1347,"💰 Seamless QR Code — spadek ceny","Seamless QR Code","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fseamless-qr-code","2026-10-23T04:15:26.351Z",{"id":1020,"type":1005,"title":1021,"body":1022,"tool_name":1023,"z2u_price":1024,"z2u_url":1025,"status":1011,"read_at":48,"created_at":1026},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":1028,"type":1005,"title":1029,"body":1030,"tool_name":1031,"z2u_price":1032,"z2u_url":1033,"status":1011,"read_at":48,"created_at":1034},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":1036,"type":1005,"title":1037,"body":1038,"tool_name":1039,"z2u_price":1009,"z2u_url":1040,"status":1011,"read_at":48,"created_at":1041},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":1043,"type":1005,"title":1044,"body":1045,"tool_name":1046,"z2u_price":1009,"z2u_url":1047,"status":1011,"read_at":48,"created_at":1048},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":1050,"type":1005,"title":1051,"body":1052,"tool_name":1053,"z2u_price":1009,"z2u_url":1054,"status":1011,"read_at":48,"created_at":1055},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":1057,"type":1005,"title":1058,"body":1059,"tool_name":1060,"z2u_price":1061,"z2u_url":1062,"status":1011,"read_at":48,"created_at":1063},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":1065,"type":1005,"title":1066,"body":1067,"tool_name":1068,"z2u_price":1009,"z2u_url":1069,"status":1011,"read_at":48,"created_at":1070},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":1072,"type":1005,"title":1073,"body":1074,"tool_name":1075,"z2u_price":1009,"z2u_url":1076,"status":1011,"read_at":48,"created_at":1077},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":1079,"type":1005,"title":1080,"body":1081,"tool_name":1082,"z2u_price":1009,"z2u_url":1083,"status":1011,"read_at":48,"created_at":1084},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":1086,"type":1005,"title":1087,"body":1088,"tool_name":1089,"z2u_price":1009,"z2u_url":1090,"status":1011,"read_at":48,"created_at":1091},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":1093,"type":1005,"title":1094,"body":1095,"tool_name":1096,"z2u_price":1097,"z2u_url":1098,"status":1011,"read_at":48,"created_at":1099},1336,"💰 Al Webcam Effects — spadek ceny","Platforma: Dealify | Kategoria: Creative | Cena: €135.95 (regular: €1134.95) | Rabat: 88%","Al Webcam Effects",135.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fal-webcam-effects","2026-10-23T04:15:19.132Z",{"id":1101,"type":1005,"title":1102,"body":1103,"tool_name":1104,"z2u_price":1009,"z2u_url":1105,"status":1011,"read_at":48,"created_at":1106},1335,"💰 Vocal — spadek ceny","Platforma: Dealify | Kategoria: Productivity | Cena: €67.95 (regular: €670.95) | Rabat: 90%","Vocal","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fvocal","2026-10-23T04:15:17.568Z",{"id":1108,"type":1005,"title":1109,"body":1103,"tool_name":1110,"z2u_price":1009,"z2u_url":1111,"status":1011,"read_at":48,"created_at":1112},1334,"💰 Vimageo — spadek ceny","Vimageo","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fvimageo","2026-10-23T04:15:17.350Z",{"id":1114,"type":1005,"title":1115,"body":1116,"tool_name":1117,"z2u_price":1032,"z2u_url":1118,"status":1011,"read_at":48,"created_at":1119},1333,"💰 Sheetany — spadek ceny","Platforma: Dealify | Kategoria: Productivity | Cena: €101.95 (regular: €395.95) | Rabat: 74%","Sheetany","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fsheetany","2026-10-23T04:15:16.584Z",{"id":1121,"type":1005,"title":1122,"body":1123,"tool_name":1124,"z2u_price":1125,"z2u_url":1126,"status":1011,"read_at":48,"created_at":1127},1332,"💰 LightPDF — spadek ceny","Platforma: Dealify | Kategoria: Productivity | Cena: €118.95 (regular: €226.95) | Rabat: 48%","LightPDF",118.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Flightpdf","2026-10-23T04:15:15.734Z",{"id":1129,"type":1005,"title":1130,"body":1131,"tool_name":1132,"z2u_price":1133,"z2u_url":1134,"status":1011,"read_at":48,"created_at":1135},1331,"💰 International Open Academy — spadek ceny","Platforma: Dealify | Kategoria: Productivity | Cena: €203.95 (regular: €453.95) | Rabat: 55%","International Open Academy",203.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Finternational-open-academy","2026-10-23T04:15:15.580Z",{"id":1137,"type":1005,"title":1138,"body":1139,"tool_name":1140,"z2u_price":1141,"z2u_url":1142,"status":1011,"read_at":48,"created_at":1143},1330,"💰 4K Bundle — spadek ceny","Platforma: Dealify | Kategoria: Productivity | Cena: €59.95 (regular: €79.95) | Rabat: 25%","4K Bundle",59.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002F4k-bundle","2026-10-23T04:15:14.911Z",{"id":1145,"type":1005,"title":1146,"body":1147,"tool_name":1148,"z2u_price":1061,"z2u_url":1149,"status":1011,"read_at":48,"created_at":1150},1329,"💰 Video Service Desk — spadek ceny","Platforma: Dealify | Kategoria: Business | Cena: €169.95 (regular: €3390.95) | Rabat: 95%","Video Service Desk","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fvideo-service-desk","2026-10-23T04:15:13.262Z",{"id":1152,"type":1005,"title":1153,"body":1154,"tool_name":1155,"z2u_price":1009,"z2u_url":1156,"status":1011,"read_at":48,"created_at":1157},1328,"💰 Classified Billing — spadek ceny","Platforma: Dealify | Kategoria: Business | Cena: €67.95 (regular: €670.95) | Rabat: 90%","Classified Billing","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fclassified-billing","2026-10-23T04:15:13.024Z",{"id":1159,"type":1005,"title":1160,"body":1161,"tool_name":1162,"z2u_price":1163,"z2u_url":1164,"status":1011,"read_at":48,"created_at":1165},1327,"💰 Paddle CRM — spadek ceny","Platforma: Dealify | Kategoria: Business | Cena: €337.95 (regular: €3373.95) | Rabat: 90%","Paddle CRM",337.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fpaddle-crm","2026-10-23T04:15:12.487Z",{"id":1167,"type":1005,"title":1168,"body":1169,"tool_name":1170,"z2u_price":1032,"z2u_url":1171,"status":1011,"read_at":48,"created_at":1172},1326,"💰 ZuckerBot — spadek ceny","Platforma: Dealify | Kategoria: Social Media | Cena: €101.95 (regular: €1010.95) | Rabat: 90%","ZuckerBot","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fzuckerbot","2026-10-23T04:15:10.775Z",{"id":1174,"type":1005,"title":1175,"body":1176,"tool_name":1177,"z2u_price":1178,"z2u_url":1179,"status":1011,"read_at":48,"created_at":1180},1325,"💰 Sociosight — spadek ceny","Platforma: Dealify | Kategoria: Social Media | Cena: €76.95 (regular: €107.95) | Rabat: 29%","Sociosight",76.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fsociosight","2026-10-23T04:15:10.636Z",{"id":1182,"type":1005,"title":1183,"body":1184,"tool_name":1185,"z2u_price":1009,"z2u_url":1186,"status":1011,"read_at":48,"created_at":1187},1324,"💰 Qura Al — spadek ceny","Platforma: Dealify | Kategoria: Social Media | Cena: €67.95 (regular: €670.95) | Rabat: 90%","Qura Al","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fqura-al","2026-10-23T04:15:10.498Z",{"id":1189,"type":1005,"title":1190,"body":1184,"tool_name":1191,"z2u_price":1009,"z2u_url":1192,"status":1011,"read_at":48,"created_at":1193},1323,"💰 Brand2Social — spadek ceny","Brand2Social","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fbrand2social","2026-10-23T04:15:10.248Z",{"id":1195,"type":1005,"title":1196,"body":1197,"tool_name":1198,"z2u_price":1061,"z2u_url":1199,"status":1011,"read_at":48,"created_at":1200},1322,"💰 Structa — spadek ceny","Platforma: Dealify | Kategoria: Developer Tools | Cena: €169.95 (regular: €339.95) | Rabat: 50%","Structa","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fstructa","2026-10-23T04:15:08.863Z",{"id":1202,"type":1005,"title":1203,"body":1204,"tool_name":1205,"z2u_price":1061,"z2u_url":1206,"status":1011,"read_at":48,"created_at":1207},1321,"💰 N8Nitro — spadek ceny","Platforma: Dealify | Kategoria: Developer Tools | Cena: €169.95 (regular: €1362.95) | Rabat: 88%","N8Nitro","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fn8nitro","2026-10-23T04:15:08.608Z",{"id":1209,"type":1005,"title":1210,"body":1211,"tool_name":1212,"z2u_price":1009,"z2u_url":1213,"status":1011,"read_at":48,"created_at":1214},1320,"💰 EasyLink Automations — spadek ceny","Platforma: Dealify | Kategoria: Developer Tools | Cena: €67.95 (regular: €670.95) | Rabat: 90%","EasyLink Automations","https:\u002F\u002Fdealify.com\u002Fproducts\u002Feasylink-automations","2026-10-23T04:15:08.256Z",{"id":1216,"type":1005,"title":1217,"body":1218,"tool_name":1219,"z2u_price":1024,"z2u_url":1220,"status":1011,"read_at":48,"created_at":1221},1319,"💰 devActivity — spadek ceny","Platforma: Dealify | Kategoria: Developer Tools | Cena: €226.95 (regular: €1124.95) | Rabat: 80%","devActivity","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fdevactivity","2026-10-23T04:15:07.997Z",{"id":1223,"type":1005,"title":1224,"body":1225,"tool_name":1226,"z2u_price":1009,"z2u_url":1227,"status":1011,"read_at":48,"created_at":1228},1318,"💰 WP Website Speedy — spadek ceny","Platforma: Dealify | Kategoria: SEO | Cena: €67.95 (regular: €670.95) | Rabat: 90%","WP Website Speedy","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fwebsite-speedy","2026-10-23T04:15:06.626Z",{"id":1230,"type":1005,"title":1231,"body":1232,"tool_name":1233,"z2u_price":1009,"z2u_url":1234,"status":1011,"read_at":48,"created_at":1235},1317,"💰 LocalAuditPro — spadek ceny","Platforma: Dealify | Kategoria: SEO | Cena: €67.95 (regular: €363.95) | Rabat: 81%","LocalAuditPro","https:\u002F\u002Fdealify.com\u002Fproducts\u002Flocalauditpro","2026-10-23T04:15:06.286Z",{"id":1237,"type":1005,"title":1238,"body":1225,"tool_name":1239,"z2u_price":1009,"z2u_url":1240,"status":1011,"read_at":48,"created_at":1241},1316,"💰 360contentOPS — spadek ceny","360contentOPS","https:\u002F\u002Fdealify.com\u002Fproducts\u002F360contentops","2026-10-23T04:15:05.741Z",{"id":1243,"type":1005,"title":1244,"body":1245,"tool_name":1246,"z2u_price":1009,"z2u_url":1247,"status":1011,"read_at":48,"created_at":1248},1315,"💰 Zenith Write — spadek ceny","Platforma: Dealify | Kategoria: AI | Cena: €67.95 (regular: €670.95) | Rabat: 90%","Zenith Write","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fzenith-write","2026-10-23T04:15:04.293Z",{"id":1250,"type":1005,"title":1251,"body":1252,"tool_name":1253,"z2u_price":1009,"z2u_url":1254,"status":1011,"read_at":48,"created_at":1255},1314,"💰 AI Mentions — spadek ceny","Platforma: Dealify | Kategoria: AI | Cena: €67.95 (regular: €112.95) | Rabat: 40%","AI Mentions","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fai-mentions","2026-10-23T04:15:03.535Z",{"id":1257,"type":1005,"title":1258,"body":1259,"tool_name":1260,"z2u_price":1009,"z2u_url":1261,"status":1011,"read_at":48,"created_at":1262},1313,"💰 Open eLMS Learning Generator — spadek ceny","Platforma: Dealify | Kategoria: AI | Cena: €67.95 (regular: €288.95) | Rabat: 76%","Open eLMS Learning Generator","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fopen-elms","2026-10-23T04:15:03.085Z",{"id":1264,"type":1005,"title":1265,"body":1245,"tool_name":1266,"z2u_price":1009,"z2u_url":1267,"status":1011,"read_at":48,"created_at":1268},1312,"💰 Agentica — spadek ceny","Agentica","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fagentica","2026-10-23T04:15:02.928Z",{"id":1270,"type":1005,"title":1029,"body":1271,"tool_name":1031,"z2u_price":1272,"z2u_url":1033,"status":1011,"read_at":48,"created_at":1273},1311,"Platforma: Dealify | Kategoria: Sales & Marketing | Cena: €100.95 (regular: €1008.95) | Rabat: 90%",100.95,"2026-10-22T04:15:24.021Z",{"id":1275,"type":1005,"title":1058,"body":1276,"tool_name":1060,"z2u_price":1277,"z2u_url":1062,"status":1011,"read_at":48,"created_at":1278},1310,"Platforma: Dealify | Kategoria: Cloud | Cena: €168.95 (regular: €396.95) | Rabat: 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90%","2026-10-15T04:15:11.677Z",{"id":1535,"type":1005,"title":1536,"body":1537,"tool_name":1538,"z2u_price":1539,"z2u_url":1540,"status":1011,"read_at":48,"created_at":1541},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":1543,"type":1005,"title":1183,"body":1544,"tool_name":1185,"z2u_price":1427,"z2u_url":1186,"status":1011,"read_at":48,"created_at":1545},1250,"Platforma: Dealify | Kategoria: Social Media | Cena: €66.95 (regular: €669.95) | Rabat: 90%","2026-10-15T04:15:09.802Z",{"id":1547,"type":1005,"title":1190,"body":1544,"tool_name":1191,"z2u_price":1427,"z2u_url":1192,"status":1011,"read_at":48,"created_at":1548},1249,"2026-10-15T04:15:09.556Z",{"data":1550},{"left":305,"right":802,"confidence":1551,"content":1552,"generatedAt":1581,"model":1582},0.64,{"h1":1553,"lead":1554,"sections":1555,"verdict":1568,"faq":1571},"Inkling vs Trint — modele AI czy transkrypcja?","Inkling to otwarty model multimodalny od Thinking Machines Lab, a Trint to komercyjna platforma do transkrypcji. Porównujemy ich możliwości, ceny i zastosowania, aby pomóc Ci wybrać narzędzie odpowiednie do Twoich potrzeb.",[1556,1559,1562,1565],{"h2":1557,"body":1558},"Czym różnią się te narzędzia?","Inkling to model AI o otwartych wagach (Apache 2.0), który przyjmuje tekst, obraz i audio, a zwraca tekst i kod. Trint to zamknięta platforma SaaS, która przekształca audio i wideo na edytowalny tekst, obsługując ponad 40 języków. Inkling daje kontrolę nad modelami i pozwala na fine-tuning, podczas gdy Trint skupia się na gotowym przepływie pracy z transkrypcją.",{"h2":1560,"body":1561},"Ile to kosztuje?","Wagi Inkling są darmowe (Apache 2.0), ale inferencja przez API partnerów kosztuje 4,05 USD za 1000 tokenów. Trint nie podaje publicznie cen — oferuje różne plany dostosowane do potrzeb, a dostępny jest darmowy okres próbny. Dla porównania: jeśli przetwarzasz dużo tokenów, koszty API Inkling mogą być znaczące, ale masz możliwość self-hostingu.",{"h2":1563,"body":1564},"Jakie są kluczowe funkcje?","Inkling oferuje multimodalne wejście (tekst, obraz, audio), kontekst do 1 miliona tokenów, regulowany wysiłek rozumowania, wywoływanie narzędzi i ustrukturyzowane wyjście. Trint zapewnia transkrypcję w ponad 40 językach z dokładnością do 99%, współpracę w czasie rzeczywistym, integracje z systemami CMS i edytorami wideo, a także napisy i tłumaczenie AI na 50+ języków. Inkling jest bardziej elastyczny, Trint bardziej ukierunkowany na produkcję treści.",{"h2":1566,"body":1567},"Kto powinien wybrać które narzędzie?","Inkling sprawdzi się u deweloperów i badaczy, którzy chcą budować własne aplikacje AI z kontrolą nad modelami. Trint jest lepszy dla dziennikarzy, podcasterów i zespołów marketingowych, które potrzebują szybkiej, dokładnej transkrypcji i narzędzi do edycji bez zagłębiania się w techniczne szczegóły.",{"pick_x":1569,"pick_y":1570},"Inkling wybierzesz, jeśli jesteś deweloperem lub badaczem, który chce pracować na otwartym modelu z możliwością fine-tuningu i self-hostingu, a także potrzebuje obsługi multimodalnej i dużego kontekstu.","Trint wybierzesz, jeśli jesteś twórcą treści, dziennikarzem lub pracujesz w zespole, który potrzebuje gotowego narzędzia do transkrypcji z funkcjami współpracy i integracjami, bez konieczności zarządzania infrastrukturą.",[1572,1575,1578],{"q":1573,"a":1574},"Czy Inkling obsługuje transkrypcję audio?","Tak, Inkling przyjmuje audio jako wejście, ale nie jest wyspecjalizowany w transkrypcji — to model ogólnego przeznaczenia, który może generować tekst na podstawie audio.",{"q":1576,"a":1577},"Czy Trint oferuje API?","Tak, Trint ma API, ale szczegóły dotyczące cen i dostępu są niepotwierdzone — sprawdź na stronie Trint.",{"q":1579,"a":1580},"Które narzędzie jest tańsze?","Inkling ma darmowe wagi, ale koszty API mogą być wysokie (4,05 USD za 1000 tokenów). Trint ma subskrypcję, ale ceny nie są publiczne — trudno jednoznacznie porównać.","2026-08-10T23:48:34.808Z","deepseek\u002Fdeepseek-v4-flash"]