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62%","MiPLR",33.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fmiplr","unread","2026-10-24T04:15:18.969Z",{"id":313,"type":314,"title":315,"body":316,"tool_name":317,"z2u_price":318,"z2u_url":319,"status":310,"read_at":48,"created_at":320},1356,"new_tool_found","🆕 Aon Pro Studio — €67.95 (-40%)","Platforma: Dealify | Kategoria: Creative | Cena: €67.95 (regular: €112.95) | Rabat: 40%","Aon Pro Studio",67.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Faon-pro-studio","2026-10-24T04:15:18.400Z",{"id":322,"type":304,"title":323,"body":324,"tool_name":325,"z2u_price":308,"z2u_url":326,"status":310,"read_at":48,"created_at":327},1355,"💰 StickyPassword — spadek ceny","Platforma: Dealify | Kategoria: Productivity | Cena: €33.95 (regular: €219.95) | Rabat: 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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.","Top Up Credits (Pay-as-you-go): $10 minimum purchase (10,000 credits). Credits never expire, purchases are stackable, transferable to other users, and usable with all models. Exchange Rate: $1 per 1,000 credits. Available Credit Packages: $10 for 10,000 credits. $20 for 20,000 credits. $30 for 30,000 credits. $50 for 50,000 credits. $100 for 100,000 credits. Basic Access: Credit balance below 5,000. Includes basic chat models, basic image generation, basic translation, and basic voice. Full Access: Credit balance of 5,000 or more. Includes all basic features plus GPT-4o, Claude and other advanced models, advanced image generation and editing, video generation, advanced voice cloning, and all advanced features. Disclaimer: Please note that pricing information may not be up to date. For the most accurate and current pricing details, refer to the official UniAI website.",[880,881],"Privacy Policy HubSpot Website Terms of Use What is UniAI?","Accuracy and Reliability: 4.3\u002F5 Ease of Use: 4.4\u002F5 Functionality and Features: 4.7\u002F5 Performance and Speed: 4.2\u002F5 Customization and Flexibility: 3.8\u002F5 Data Privacy and Security: 4.0\u002F5 Support and Resources: 3.7\u002F5 Cost-Efficiency: 4.4\u002F5 Integration Capabilities: 3.9\u002F5 Overall Score: 4.2\u002F5",[883,884,885,886,887,888,889,890,891,892],"Promote UniAI","What is UniAI?","Key Features:","Pros","Cons","Who is Using UniAI?","Pricing:","What Makes UniAI Unique?","How We Rated It:","One Workspace For Every Major Model:",[],[895,896,897,898,899,900,901,902,903,904],"Wiele modeli w jednym logowaniu i wspólnych kredytach","Kompletne środowisko: chat, design, image, video, translation i audio","Workflowy agentowe do researchu, slajdów, analizy danych i plików","Spójna praca na desktopie, w 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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 > UniAI UniAI\nRated 0 out of 5 (0) Verified Create designs, videos across 100 models.\nAI Categories: AI agents , image generators , translator \nPricing Model: Freemium , $$10\u002F10,000 credits 1 Visit Site What is UniAI UniAI Reviews UniAI 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 UniAI\nFeatured on Futurepedia 1 Copy Embed Code Write a review Related Categories\nUniAI 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 UniAI?\nUniAI is a unified AI workspace that puts more than 100 leading models for chat, design, translation, and media creation behind a single account and credit system. It is built for knowledge workers, creators, and teams that bounce between different AI apps. Instead of managing separate subscriptions and histories, they can switch models per task and move work across chat, design, image, video, text, and audio in one place.\nKey Features:\nUnified Chat & Agent Workspace: Chat with GPT, Claude, Gemini, DeepSeek and other models, then hand tasks to an agent that can research the web, build slide decks, analyze spreadsheets, and organize local files. AI Design Agent on Infinite Canvas: Describe a design in plain language and the Design Agent lays out a multi layer canvas that users can still tweak by hand and export as PNG, PDF, or SVG. Create Hub for Image & Video: Generate images, short videos, animations, and music videos from text or images, with templates for quick clips, story arcs, and AI shorts. Translation & Writing Suite: Translate text, documents, and subtitles across 100+ languages while keeping formatting and subtitle timing, and use dedicated pages for writing, smart replies, grammar fixes, and long document summaries. Image & Audio Processing Tools: Edit, upscale, restore, and translate images, plus run multi voice text to speech, speech to text, and one click podcast creation. Cross Platform Access & CLI: Use a desktop client, browser sidebar, mobile app, and command line tool that all share a single credit pool, history, and team workspace. \nPros\nMany models, one account: More than 100 major AI models are available behind a single login and shared credits.\nEnd to end toolbox: Chat, design, image, video, translation, text, and audio tools live in one environment instead of separate apps.\nAgent driven workflows: Desktop and browser agents handle research, slide creation, data analysis, and file tasks with persistent context.\nCoverage across devices: Desktop app, browser extension, mobile client, and CLI keep the same projects and balances in sync.\nReady for small teams: Team workspaces share credit pools, asset libraries, and conversation history with role based access.\nCons\nLearning curve for new users: The number of modules and surfaces can feel complex for people who only want a basic chatbot.\nCredit based budgeting: A shared credit pool that drains faster on premium models can make monthly costs harder to predict.\nEnterprise detail is light publicly: Marketing copy mentions encryption and data ownership but not deeper items like SSO or data residency, so larger buyers may need extra checks.\nWho is Using UniAI?\nMarketing and content teams: Use Chat, Design, Create, and Text to research topics, draft campaigns, and generate visuals and clips. Product managers and analysts: Use agents for web research, spreadsheet analysis, and creating internal slide decks from prompts. Designers and video creators: Use the Design Agent and Create hub to prototype layouts, images, and short videos without leaving one workspace. Localization and support teams: Use Translate and Text to localize documents, subtitles, and help articles while preserving formatting. Developers and technical power users: Use the CLI and desktop agent alongside tools like Cursor or Claude Code to script image, video, and audio generation. Uncommon Use Cases: Faculty turning long readings and lectures into bilingual summaries and podcasts; finance or operations teams translating and summarizing reports, then auto building slide overviews. \nPricing:\nTop Up Credits (Pay-as-you-go): $10 minimum purchase (10,000 credits). Credits never expire, purchases are stackable, transferable to other users, and usable with all models. Exchange Rate: $1 per 1,000 credits. Available Credit Packages: $10 for 10,000 credits. $20 for 20,000 credits. $30 for 30,000 credits. $50 for 50,000 credits. $100 for 100,000 credits. Basic Access: Credit balance below 5,000. Includes basic chat models, basic image generation, basic translation, and basic voice. Full Access: Credit balance of 5,000 or more. Includes all basic features plus GPT-4o, Claude and other advanced models, advanced image generation and editing, video generation, advanced voice cloning, and all advanced features. Disclaimer: Please note that pricing information may not be up to date. For the most accurate and current pricing details, refer to the official UniAI website. \nWhat Makes UniAI Unique?\nUniAI does more than put many models into a single chat box. It combines a multi model agent, infinite canvas design, full image and video generation, advanced translation, and audio tools with a shared credit pool that spans desktop, browser, mobile, and CLI. That combination of deep creative tooling, research agents, and cross device access in one multi model account is still rare among AI aggregators.\nHow We Rated It:\nAccuracy and Reliability: 4.3\u002F5 Ease of Use: 4.4\u002F5 Functionality and Features: 4.7\u002F5 Performance and Speed: 4.2\u002F5 Customization and Flexibility: 3.8\u002F5 Data Privacy and Security: 4.0\u002F5 Support and Resources: 3.7\u002F5 Cost-Efficiency: 4.4\u002F5 Integration Capabilities: 3.9\u002F5 Overall Score: 4.2\u002F5 \nOne Workspace For Every Major Model:\nUniAI gives power users and teams a single place to work with many leading AI models while covering day to day needs from research and writing to design, video, translation, and audio. It is strongest for people who actually use several of these workflows and benefit from a shared credit pool and history. Those who only need a simple chatbot or strict enterprise controls may find it more than they need, but for multi modal AI work it is a compelling hub.","2026-08-09T00:09:08.992Z",200,"text\u002Fhtml; charset=utf-8","7bc1107740e16c1c32634b0e8c6d71174e09e7bb648abddce23d82cb75df12b3","data\u002Fcatalog\u002Fcache\u002Ffuturepedia\u002F65a1eebfb5f28c1b13d8db43f8481915cf78033b0671d425fa8c7a391b42c457.html",true,"human_verified","https:\u002F\u002Fwww.uniai.ai\u002F","unknown",[],"4.2\u002F5",[971,972,973,974,975,976,977,978,979,980,981,982,904],"Unified Chat & Agent Workspace","AI Design Agent on Infinite Canvas","Create Hub for Image & Video","Translation & Writing Suite","Image & Audio Processing Tools","Cross Platform Access & CLI","Unified Chat & Agent Workspace: Chat with GPT, Claude, Gemini, DeepSeek and other models, then hand tasks to an agent that can research the web, build slide decks, analyze spreadsheets, and organize local files.","AI Design Agent on Infinite Canvas: Describe a design in plain language and the Design Agent lays out a multi layer canvas that users can still tweak by hand and export as PNG, PDF, or SVG.","Create Hub for Image & Video: Generate images, short videos, animations, and music videos from text or images, with templates for quick clips, story arcs, and AI shorts.","Translation & Writing Suite: Translate text, documents, and subtitles across 100+ languages while keeping formatting and subtitle timing, and use dedicated pages for writing, smart replies, grammar fixes, and long document summaries.","Image & Audio Processing Tools: Edit, upscale, restore, and translate images, plus run multi voice text to speech, speech to text, and one click podcast creation.","Cross Platform Access & CLI: Use a desktop client, browser sidebar, mobile app, and command line tool that all share a single credit pool, history, and team workspace.","Basic Access: Credit balance below 5,000. Includes basic chat models, basic image generation, basic translation, and basic voice.",[],[986],"100+ languages",[988,989,990,991,992,993,994],"Brak publicznych informacji o API","Brak publicznych informacji o self-hostingu","Brak publicznych informacji o regionie danych","Brak publicznych informacji o zgodności z GDPR\u002FRODO","Brak publicznych informacji o licencji","Publiczna strona nie podaje vendor name ani daty wydania","Model kredytowy może być trudny do oszacowania kosztowo",[996,997,998,999,1000],"GPT-4o","Claude","Gemini","DeepSeek","GPT","Nie wykazano",[1003,1009,1012,1015,1018,1021],{"plan_name":1004,"access_model":967,"price":1005,"currency":1006,"billing_period":1007,"included_usage":1008,"trial_days":967,"card_required":967,"source_evidence":967},"Top Up Credits","$10 minimum","USD","one-time \u002F pay-as-you-go","10,000 credits",{"plan_name":1004,"access_model":967,"price":1010,"currency":1006,"billing_period":1007,"included_usage":1011,"trial_days":967,"card_required":967,"source_evidence":967},"$20","20,000 credits",{"plan_name":1004,"access_model":967,"price":1013,"currency":1006,"billing_period":1007,"included_usage":1014,"trial_days":967,"card_required":967,"source_evidence":967},"$30","30,000 credits",{"plan_name":1004,"access_model":967,"price":1016,"currency":1006,"billing_period":1007,"included_usage":1017,"trial_days":967,"card_required":967,"source_evidence":967},"$50","50,000 credits",{"plan_name":1004,"access_model":967,"price":1019,"currency":1006,"billing_period":1007,"included_usage":1020,"trial_days":967,"card_required":967,"source_evidence":967},"$100","100,000 credits",{"plan_name":1004,"access_model":870,"price":1022,"currency":1006,"billing_period":1023,"included_usage":1008,"trial_days":967,"card_required":1024,"source_evidence":864},"$10","pay-as-you-go","no","Freemium z kredytami typu pay-as-you-go. Minimalny zakup to $10 za 10,000 kredytów; 1 USD = 1,000 kredytów. Kredyty nie wygasają i są stackable\u002Ftransferable.","Brak publicznie potwierdzonych informacji o tym, czy dane są używane do trenowania modeli. Strona Futurepedia wspomina jedynie o szyfrowaniu i własności danych, ale nie podaje pełnej polityki prywatności.",[],"UniAI to zunifikowany workspace AI opisany jako narzędzie do tworzenia projektów i wideo na ponad 100 modelach. Futurepedia podaje freemium i system kredytowy, zestaw funkcji obejmujący chat\u002Fagent, design, generowanie obrazów i wideo, tłumaczenia oraz narzędzia audio, a także ocenę 4.2\u002F5. Brak jest na stronie potwierdzonych danych o vendorze, API, self-hostingu, prywatności treningowej, regionie danych, GDPR i dacie wydania.","Publiczna strona wspomina o szyfrowaniu i własności danych, ale nie zawiera szczegółów bezpieczeństwa, takich jak SSO, data residency czy formalne certyfikacje.","UniAI to zunifikowany workspace AI do projektowania, tworzenia wideo, tłumaczeń i pracy na ponad 100 modelach.",[1032,1033,1034,1035,1036],"desktop","browser","mobile","CLI","command line",[1038,1039,1040,1041,1042,1043,1044,1045,1046,1047,1048,1049,1050],"knowledge workers","creators","teams","marketing and content teams","product managers and analysts","designers and video creators","localization and support teams","developers and technical power users","Marketing and content teams","Product managers and analysts","Designers and video creators","Localization and support teams","Developers and technical power users",[1052,1053,1054,1055,1056,1057,1058,1059,1060,1061,1062,1063,1064,1065,1066],"research i automatyzacja zadań przez agenta","tworzenie designów na infinite canvas","generowanie obrazów, krótkich wideo, animacji i music videos","tłumaczenie dokumentów, podpisów i treści na 100+ języków","pisanie, smart replies, grammar fixes i podsumowania długich dokumentów","edycja, upscale, restore i translation obrazów","text-to-speech, speech-to-text i tworzenie podcastów","budowanie slajdów i analiza arkuszy kalkulacyjnych","lokalizacja i wsparcie klienta","Marketing and content teams: Use Chat, Design, Create, and Text to research topics, draft campaigns, and generate visuals and clips.","Product managers and analysts: Use agents for web research, spreadsheet analysis, and creating internal slide decks from prompts.","Designers and video creators: Use the Design Agent and Create hub to prototype layouts, images, and short videos without leaving one workspace.","Localization and support teams: Use Translate and Text to localize documents, subtitles, and help articles while preserving formatting.","Developers and technical power users: Use the CLI and desktop agent alongside tools like Cursor or Claude Code to script image, video, and audio generation.","Uncommon Use Cases: Faculty turning long readings and lectures into bilingual summaries and podcasts; finance or operations teams translating and summarizing reports, then auto building slide overviews.","published","2026-08-12T18:22:13.917Z",[1070,1071,1072,1073,1074,1075,1076,1077],"ChatGPT","Windsurf","ZenCall.ai","AutoGPT","Softr","AgentGPT","Chatbase","Baby AGI",{"checked_at":1068,"ready":964,"failed":1079},[],"Główne funkcje dotyczą projektowania graficznego i generowania obrazów\u002Fwideo","```markdown\n# **UniAI – Zunifikowany Workspace AI dla Kreatywności i Automatyzacji**\n\n**UniAI** to wszechstronne narzędzie AI łączące generowanie obrazów, projektowanie, tworzenie wideo, tłumaczenia i pracę na ponad **100 modelach sztucznej inteligencji** w jednym intuicyjnym środowisku. To idealne rozwiązanie dla designerów, twórców treści i zespołów, które potrzebują **jednego miejsca do kreatywnej pracy z AI** – od prostych grafik po zaawansowane animacje i automatyzację zadań.\n\n---\n\n## **🔥 Najważniejsze funkcje UniAI**\n\n1. **Unified Chat & Agent Workspace**\n   - Intuicyjny interfejs do komunikacji z wieloma modelami AI (np. chatboty, agenci automatyzujące zadania).\n   - Możliwość **badania i analizy danych** z wykorzystaniem agentów AI, które mogą przyspieszać pracę nad projektami.\n\n2. **AI Design Agent na Infinite Canvas**\n   - **Nieskończona płótno** do swobodnego projektowania z wsparciem AI – od szkiców po gotowe projekty graficzne.\n   - Generowanie i edycja elementów wizualnych w czasie rzeczywistym, z integracją z innymi narzędziami UniAI.\n\n3. **Create Hub – Generowanie Obrazów & Wideo**\n   - Tworzenie **obrazów, krótkich wideo, animacji i teledysków** za pomocą zaawansowanych modeli (np. Stable Diffusion, DALL·E).\n   - Opcje dostosowywania stylu, rozdzielczości i formatu wyjściowego.\n\n4. **Translation & Writing Suite**\n   - **Tłumaczenia tekstów** w wielu językach (w tym polski) z zachowaniem kontekstu.\n   - Narzędzia do pisania i edycji treści (np. generowanie opisów, korekta stylistyczna).\n\n5. **Image & Audio Processing Tools**\n   - Edycja zdjęć (usunięcie tła, retusz, zmiana rozmiaru) i przetwarzanie audio (np. czyszczenie ścieżek dźwiękowych).\n   - Możliwość generowania **muzyki i efektów dźwiękowych** do projektów wideo.\n\n---\n\n## **🎯 Dla kogo jest UniAI?**\n\nUniAI sprawdzi się w pracy:\n✅ **Designerów i grafików** – dzięki **Infinite Canvas** i narzędziom do generowania obrazów.\n✅ **Twórców treści i marketerów** – do szybkiego tworzenia grafik, wideo i tłumaczeń.\n✅ **Programistów i badaczy** – dzięki **agentom AI** automatyzującym analizę danych i kodowanie.\n✅ **Muzyków i filmowców** – do generowania **animacji, teledysków i ścieżek dźwiękowych**.\n✅ **Freelancerów i małe zespoły** – jako **wszechstronny workspace** zastępujący wiele oddzielnych narzędzi.\n\n---\n\n## **💰 Cena i darmowy plan**\n\nUniAI działa w modelu **freemium** z systemem kredytów:\n\n- **Darmowy plan (Basic Access)**\n  - Dostępny przy **saldo poniżej 5,000 kredytów**.\n  - Obejmuje: podstawowe modele chatowe, generowanie obrazów, tłumaczenia i narzędzia głosowe (basic tier).\n  - **Ograniczenia**: brak zaawansowanych modeli i funkcji premium.\n\n- **Płatne kredyty (Pay-as-you-go)**\n  - **1 USD = 1,000 kredytów**.\n  - Minimalny zakup: **$10 za 10,000 kredytów**.\n  - **Kredyty nie wygasają**, są **kumulowalne** i **możliwe do przeniesienia** między projektami.\n\n💡 *Przykładowe zużycie*:\n- Generowanie 1 obrazka (SDXL) ~50-100 kredytów.\n- Tłumaczenie 1,000 znaków ~10 kredytów.\n\n---\n\n## **⚖️ Zalety i wady UniAI**\n\n### **✅ Zalety**\n1. **Wszechstronność** – jedno narzędzie zastępuje wiele aplikacji (design, wideo, tłumaczenia, audio).\n2. **Infinite Canvas** – unikalne rozwiązanie dla designerów, którzy potrzebują **swobody tworzenia**.\n3. **Elastyczny model płatności** – kredyty nie wygasają, można je gromadzić i transferować.\n\n### **❌ Wady**\n1. **Ograniczenia darmowego planu** – podstawowe funkcje mogą być niewystarczające dla profesjonalistów.\n2. **Koszt zaawansowanych funkcji** – generowanie wysokiej jakości wideo lub używanie topowych modeli może szybko wyczerpać kredyty.\n3. **Krzywa uczenia** – ze względu na wiele funkcji, początkujący użytkownicy mogą potrzebować czasu na opanowanie narzędzia.\n\n---\n\n## **🏆 Werdykt: Czy warto?**\n\n**UniAI to potężne narzędzie dla tych, którzy szukają jednego workspace’u do kreatywnej pracy z AI** – od projektowania po wideo i automatyzację. Jeśli potrzebujesz **elastyczności i wielu funkcji w jednym miejscu**, a nie przeszkadza Ci model płatności oparty na kredytach, **warto przetestować**. Dla początkujących lub okazjonalnych użytkowników **darmowy plan może być wystarczający**, ale profesjonaliści docenią płatne opcje.\n\n🔹 **Polecamy dla:** designerów, twórców treści i zespołów, które chcą zoptymalizować workflow z AI.\n🔹 **Alternatywy:** MidJourney (obrazki), Runway (wideo), ale **UniAI łączy je wszystkie w jednym miejscu**.\n```",{"source_page_url":864,"final_url":864,"fetched_at":959},{"alternatives_count":184,"active_deals":184},[],[],{"data":1087,"_meta":1569,"alternatives":1570,"deals":1571},{"source":1088,"source_key":1089,"source_page_url":1090,"name":1091,"slug":1092,"description":1093,"title":1094,"category":869,"access_model":1095,"pricing_evidence":1096,"capability_evidence":1100,"headings":1111,"breadcrumbs":1116,"pros":1117,"cons":1129,"external_links":1134,"affiliate_links":1194,"official_link_candidates":1195,"text_excerpt":1206,"fetched_at":1207,"final_url":1090,"http_status":960,"content_type":1208,"content_hash":1209,"cache_file":1210,"published":964,"verification_status":965,"official_url":1211,"official_evidence":1212,"api_available":1455,"blog_titles":1456,"data_region":967,"data_training":1460,"entity_type":1461,"external_rating":967,"features":1462,"free_summary":1481,"gdpr":967,"integrations":1482,"languages":1498,"license":1501,"limitations":1502,"model_names":1511,"polish_support":1001,"pricing_plans":1513,"pricing_summary":1519,"privacy_summary":1520,"release_date":1521,"research_links":1522,"research_summary":1523,"security_summary":1524,"self_hosted":1455,"short_description":1525,"starting_price":1517,"supported_platforms":1526,"target_users":1533,"trial_summary":967,"use_cases":1541,"vendor":1557,"status":1067,"last_verified_at":1558,"alternatives":1559,"quality":1564,"category_v3":265,"category_v3_reason":1566,"deep_card":1567,"deep_card_generated":964,"category_v3_main":259,"category_v3_label":266,"category_v3_main_label":260,"_category_fixed":964,"evidence":1568},"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","open_source",[1097,1098,1099],"97 Visiter Freemium Kimi K3","248 Visiter Freemium GPT‑5.5 Instant","89 Visiter Freemium Claude Sonnet 5",[1101,1102,1103,1104,1105,1106,1107,1108,1109,1110],"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)",[1112,1113,1114,1115],"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",[],[1118,1119,1120,1121,1122,1123,1124,1125,1126,1127,1128],"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",[1130,1131,1132,1133],"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.",[1135,1137,1139,1141,1143,1145,1148,1151,1154,1157,1160,1163,1165,1167,1170,1172,1173,1176,1177,1178,1181,1183,1184,1186,1188,1190,1193],{"url":1136,"text":915},"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":1138,"text":915},"https:\u002F\u002Fwww.facebook.com\u002Fsharer\u002Fsharer.php?u=https%3A%2F%2Fwww.aixploria.com%2Finkling-thinking-machines%2F",{"url":1140,"text":915},"https:\u002F\u002Fwww.linkedin.com\u002FshareArticle?url=https%3A%2F%2Fwww.aixploria.com%2Finkling-thinking-machines%2F&title=Inkling",{"url":1142,"text":915},"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":1144,"text":1070},"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.",{"url":1146,"text":1147},"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":1149,"text":1150},"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002Finkling","Hugging Face",{"url":1152,"text":1153},"https:\u002F\u002Ftinker.thinkingmachines.ai\u002Fplayground","Playground",{"url":1155,"text":1156},"https:\u002F\u002Fthinkingmachines.ai\u002Fnews\u002Fintroducing-inkling\u002F","Info",{"url":1158,"text":1159},"https:\u002F\u002Ffr.wikipedia.org\u002Fwiki\u002FGrand_mod%C3%A8le_de_langage","modèle LLM",{"url":1161,"text":1162},"https:\u002F\u002Fget.emergent.sh\u002Fe8utricukk6y","Emergent AI",{"url":1161,"text":1164},"« 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":1161,"text":1166},"Visiter",{"url":1168,"text":1169},"https:\u002F\u002Ftry.web.clickup.com\u002Fgslcwsp7r8e2","ClickUp",{"url":1168,"text":1171},"« 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":1168,"text":1166},{"url":1174,"text":1175},"https:\u002F\u002Fwww.thumbnailcreator.com\u002F","ThumbnailCreator.com",{"url":1174,"text":1107},{"url":1174,"text":1166},{"url":1179,"text":1180},"https:\u002F\u002Fvmake.ai\u002Fvideo-enhancer","Vmake Video Enhancer",{"url":1179,"text":1182},"« 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":1179,"text":1166},{"url":1185,"text":915},"https:\u002F\u002Ftwitter.com\u002FAixploria",{"url":1187,"text":915},"https:\u002F\u002Fwww.youtube.com\u002F@aixploria-off?sub_confirmation=1",{"url":1189,"text":915},"https:\u002F\u002Fwww.tiktok.com\u002F@aixploria",{"url":1191,"text":1192},"https:\u002F\u002Fcookiedatabase.org\u002Ftcf\u002Fpurposes\u002F","En savoir plus sur ces finalités",{"url":1191,"text":1192},[],[1196,1197,1198,1199,1200,1201,1202,1203,1204,1205],{"url":1142,"text":915},{"url":1144,"text":1070},{"url":1146,"text":1147},{"url":1149,"text":1150},{"url":1152,"text":1153},{"url":1155,"text":1156},{"url":1158,"text":1159},{"url":1161,"text":1162},{"url":1161,"text":1164},{"url":1161,"text":1166},"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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En l’absence d’une assignation à comparaître, d’une conformité volontaire de la part de votre fournisseur d’accès à internet ou d’enregistrements supplémentaires provenant d’une tierce partie, les informations stockées ou extra","2026-08-09T00:14:43.170Z","text\u002Fhtml; charset=UTF-8","2c6190c18f5addd48c5cca6607e605bccb20d776de5a38e0e597d41861e294d3","data\u002Fcatalog\u002Fcache\u002Faixploria\u002Fbafa5f6cf2cf2288f618d82e718d5047b8c787281b3fbadc6d2f41fd66873605.html","https:\u002F\u002Fthinkingmachines.ai",{"url":1149,"final_url":1213,"status":960,"fetched_at":1214,"content_hash":1215,"title":1216,"description":1217,"headings":1218,"pricing_evidence":1238,"privacy_evidence":1248,"capability_evidence":1253,"text_excerpt":1260,"linked_pages":1261,"research_links":1448,"blog_titles":1451,"use_case_evidence":1453,"feature_evidence":1454},"https:\u002F\u002Fhuggingface.co\u002Fthinkingmachines\u002FInkling","2026-08-09T01:07:48.463Z","071b65bbe9b71bd6c6fa9ff0eaad27a0cffa487372c816a79082d221fb72f273","thinkingmachines\u002FInkling · Hugging Face","We’re on a journey to advance and democratize artificial intelligence through open source and open science.",[1219,1220,1091,1221,1222,1223,1224,1225,1226,1227,1228,1229,1230,1231,1232,1233,1234,1235,1236,1091,1237],"thinkingmachines \u002F Inkling like 1.7k Follow Thinking Machines Lab 1.65k","Instructions to use thinkingmachines\u002FInkling with libraries, inference providers, notebooks, and local apps. 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",[1239,1240,1233,1241,1242,1243,1244,1245,1246,1247],"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",[1239,1249,1250,1240,1230,1251,1252,1241,1242,1244,1245,1246,1247],"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.",[1239,1254,1254,1254,1255,1256,1257,1258,1241,1259],"# 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",[1262,1279,1296,1341,1376,1391,1423,1442],{"url":1263,"kind":1264,"anchor":1265,"title":1266,"description":1217,"headings":1267,"feature_evidence":1269,"use_case_evidence":1276,"fetched_at":1277,"status":960,"content_hash":1278},"https:\u002F\u002Fhuggingface.co\u002Fdocs","feature","Docs","Hugging Face - Documentation",[1268],"Documentation",[1270,1271,1272,1273,1274,1275],"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",[1270],"2026-08-09T01:07:44.900Z","990104750e302df42cfbe57c0e9bda24a99fe40a002a982fabe2c01f891a38ce",{"url":1280,"kind":1264,"anchor":915,"title":1281,"description":1217,"headings":1282,"feature_evidence":1290,"use_case_evidence":1293,"fetched_at":1294,"status":960,"content_hash":1295},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fsafetensors","Safetensors · Hugging Face",[1283,1283,1284,1285,1286,1287,1288,1289],"Safetensors","Installation","Usage","Load tensors","Save tensors","Format","Featured Projects",[1291,1292],"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",[1291],"2026-08-09T01:07:45.451Z","c698daac80d92308eba5b562cc1bf12e1cba86f6ef0b3e5bad9251fbae968243",{"url":1297,"kind":1264,"anchor":1298,"title":1299,"description":1217,"headings":1300,"feature_evidence":1318,"use_case_evidence":1337,"fetched_at":1339,"status":960,"content_hash":1340},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Finference-providers","NEW","Inference Providers · Hugging Face",[1301,1301,1302,1303,1304,1305,1306,1307,1308,1309,1310,1311,1312,1313,1310,1311,1314,1315,1316,1317],"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)",[1319,1320,1321,1322,1323,1324,1325,1326,1327,1328,1329,1330,1331,1315,1332,1333,1334,1335,1336],"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",[1319,1326,1338,1328],"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":1342,"kind":1264,"anchor":915,"title":1343,"description":1217,"headings":1344,"feature_evidence":1365,"use_case_evidence":1373,"fetched_at":1374,"status":960,"content_hash":1375},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fmodel-cards#specifying-a-base-model","Model Cards · Hugging Face",[1345,1346,1347,1348,1349,1350,1351,1352,1353,1354,1355,1356,1357,1358,1359,1360,1361,1362,1363,1364],"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?",[1366,1367,1368,1369,1370,1371,1372],"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.",[1366],"2026-08-09T01:07:46.209Z","4a2649199209784210d6ea49562458af1f67c515f1dc756887ad90e4fc72a4c6",{"url":1377,"kind":1264,"anchor":915,"title":1378,"description":1217,"headings":1379,"feature_evidence":1386,"use_case_evidence":1388,"fetched_at":1389,"status":960,"content_hash":1390},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Feval-results","Evaluation Results · Hugging Face",[1345,1359,1380,1381,1382,1383,1384,1385],"Benchmark Datasets","Model Evaluation Results","Adding Evaluation Results","Community Contributions","Registering a Benchmark","Eval.yaml specification",[1366,1387],"🏡 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",[1366],"2026-08-09T01:07:46.640Z","56903532887cc529d57f773b5b03a03571e7b8d2432993ef10249939d1502d20",{"url":1392,"kind":1393,"anchor":1394,"title":1395,"description":1396,"headings":1397,"feature_evidence":1417,"use_case_evidence":1420,"fetched_at":1421,"status":960,"content_hash":1422},"https:\u002F\u002Fhuggingface.co\u002Fpapers","research","Daily Papers","Daily Papers - Hugging Face","Your daily dose of AI research from AK",[1394,1398,1399,1400,1401,1402,1403,1404,1405,1406,1407,1408,1409,1410,1411,1412,1413,1414,1415,1416],"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",[1418,1419],"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",[1418],"2026-08-09T01:07:47.106Z","81ef1f8e47d94fcea90328e6f9de0dfd619225623f7b00abb0d70324368cf872",{"url":1424,"kind":1425,"title":1424,"description":915,"feature_evidence":1426,"use_case_evidence":1439,"fetched_at":1440,"status":960,"content_hash":1441},"https:\u002F\u002Fhuggingface.co\u002Fsitemap-blog.xml","blog",[1427,1428,1429,1430,1431,1432,1433,1434,1435,1436,1437,1438],"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":1443,"kind":1393,"title":1443,"description":915,"feature_evidence":1444,"use_case_evidence":1445,"fetched_at":1446,"status":960,"content_hash":1447},"https:\u002F\u002Fhuggingface.co\u002Fsitemap-papers.xml",[],[],"2026-08-09T01:07:48.311Z","ad2833e02848abff8ab6f61e4988203adb61395a39ac87b4aad5879cda9bddf4",[1449,1450],{"url":1392,"title":1394,"kind":1393},{"url":1443,"title":1443,"kind":1393},[1452],{"title":1424,"url":1424,"fetched_at":1440},[],[1239,1254,1254,1254,1255,1256,1257,1258,1241,1259,1270,1271,1272,1273,1274,1275,1291,1292,1319,1320,1321,1322,1323,1324,1325,1326,1327,1328,1329,1330,1331,1315,1332,1333,1334,1335,1336,1366,1367,1368,1369,1370,1371,1372,1366,1387],"yes",[1457],{"fetched_at":1458,"title":1459,"url":1155},"2026-07-23T17:27:17.000Z","Introducing Inkling: A New Era of Open-Weights AI","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",[1463,1464,1465,1466,1467,1468,1469,1470,1471,1472,1473,1474,1475,1476,1477,1478,1479,1480],"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.",[1483,1484,1485,1486,1487,1488,1489,1490,1491,1492,1493,1494,1150,1495,1496,1497],"Tinker Playground","Tinker Cookbook","TogetherAI","Fireworks","Modal","Databricks","Baseten","SGLang","vLLM","TokenSpeed","Unsloth","Hugging Face Transformers","Tinker","Together AI","Fireworks AI",[1499,1500],"English","other languages","Apache 2.0",[1503,1504,1505,1506,1507,1508,1509,1510],"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",[1091,1512],"Inkling-Small",[1514],{"plan_name":1515,"access_model":1516,"price":1517,"currency":1006,"billing_period":1518,"included_usage":967,"trial_days":967,"card_required":1024,"source_evidence":1213},"Inference via API partners","open_weights","4.05","per_token","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.",[1527,1528,1529,1150,1495,1530,1531,1532],"Web","self-hosted Linux\u002FGPU environments","partner inference platforms","llama.cpp","Ollama","LM Studio",[1534,1535,1536,1537,1538,1539,1540],"developers","researchers","ML engineers","teams building agentic systems","organizations customizing foundation models","enterprise teams","AI engineers",[1542,1543,1544,1545,1546,1547,1548,1549,1550,1393,1551,1552,1553,1554,1555,1556],"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","2026-08-12T18:20:31.057Z",[1512,1560,1561,1562,1563],"DeepSeek V3","Nemotron 3 Ultra","GLM 5.2","Kimi K2.5",{"checked_at":1558,"ready":964,"failed":1565},[],"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":1090,"final_url":1090,"fetched_at":1207},{"alternatives_count":184,"active_deals":184},[],[],{"data":1573},{"left":1091,"right":865,"confidence":1574,"content":1575,"generatedAt":1604,"model":1605},0.64,{"h1":1576,"lead":1577,"sections":1578,"verdict":1591,"faq":1594},"Inkling vs UniAI — model AI czy platforma kreatywna?","Porównujemy Inkling, otwarty model multimodalny 975B, z UniAI, platformą łączącą czat, agenta i projektowanie. Sprawdź, które narzędzie lepiej pasuje do Twoich zadań — od głębokiej analizy po tworzenie grafik i wideo.",[1579,1582,1585,1588],{"h2":1580,"body":1581},"Czym się różnią Inkling i UniAI?","Inkling to model AI o 975 miliardach parametrów, który przyjmuje tekst, obrazy i audio, oferując kontekst 1 miliona tokenów. Jego wagi są otwarte na licencji Apache 2.0, a inferencja przez partnerów API kosztuje 4,05 USD za 1000 tokenów. UniAI to platforma, która daje dostęp do ponad 100 modeli (w tym GPT, Claude, Gemini) w jednym interfejsie, z agentem AI na nieskończonym płótnie do projektowania, generowaniem obrazów i wideo oraz narzędziami do tłumaczenia i edycji. Cena UniAI zaczyna się od 10 USD za 10 000 kredytów (1 USD = 1000 kredytów).",{"h2":1583,"body":1584},"Które narzędzie ma więcej funkcji?","UniAI oferuje szerszy wachlarz funkcji: czat z wieloma modelami, agenta AI na płótnie, generowanie obrazów, wideo, animacji, muzyki, tłumaczenie na 100+ języków, edycję i przywracanie zdjęć, zamianę tekstu na mowę i odwrotnie, a także agentów przeglądarki i pulpitu. Inkling skupia się na głębokim przetwarzaniu multimodalnym z regulowanym wysiłkiem rozumowania i wsparciem dla wywoływania narzędzi oraz strukturalnego wyjścia. UniAI ma przewagę w kreatywnych i produktywnych zadaniach, Inkling w analityce i modelowaniu.",{"h2":1586,"body":1587},"Jak wypadają ceny i limity?","Inkling kosztuje 4,05 USD za 1000 tokenów przy inferencji przez partnerów API, co przy kontekście 1M tokenów może być kosztowne przy długich sesjach. UniAI działa w systemie kredytowym: 10 USD daje 10 000 kredytów, a różne modele i funkcje pobierają różne stawki (np. premium modele szybciej zużywają kredyty). UniAI nie ma limitu kontekstu per se, ale zależy od modelu, z którego korzystasz. Inkling oferuje darmowe wagi do samodzielnego hostowania, co eliminuje opłaty za API, ale wymaga własnej infrastruktury.",{"h2":1589,"body":1590},"Dla kogo jest Inkling, a dla kogo UniAI?","Inkling jest dla badaczy i developerów, którzy potrzebują otwartego, multimodalnego modelu z dużym kontekstem i możliwością dostrajania (fine-tuning przez Tinker). UniAI jest dla twórców treści, marketerów i małych zespołów, które chcą w jednym miejscu projektować, generować multimedia i automatyzować zadania bez żonglowania wieloma aplikacjami.",{"pick_x":1592,"pick_y":1593},"Inkling wybierzesz, jeśli potrzebujesz otwartego modelu do głębokiej analizy multimodalnej, masz własne GPU do hostowania i chcesz dostrajać model pod specyficzne zadania.","UniAI wybierzesz, jeśli szukasz wszechstronnej platformy do kreatywnej pracy i automatyzacji, nie chcesz martwić się infrastrukturą i wolisz płacić za kredyty zamiast za tokeny.",[1595,1598,1601],{"q":1596,"a":1597},"Czy Inkling można używać za darmo?","Tak, wagi modelu są dostępne za darmo na licencji Apache 2.0, ale musisz samodzielnie hostować model na własnym sprzęcie. Inferencja przez partnerów API jest płatna.",{"q":1599,"a":1600},"Czy UniAI obsługuje polski?","UniAI wspiera tłumaczenie na 100+ języków, w tym polski, a modele takie jak GPT i Gemini mają dobrą znajomość polskiego. Interfejs platformy jest w języku angielskim.",{"q":1602,"a":1603},"Które narzędzie jest lepsze do generowania wideo?","UniAI ma dedykowany moduł Create Hub do generowania wideo, animacji i klipów muzycznych. Inkling nie oferuje generowania wideo – skupia się na analizie i przetwarzaniu multimodalnym.","2026-08-11T02:35:12.416Z","deepseek\u002Fdeepseek-v4-flash"]