{"version":"1.0","type":"card","id":"6cfe3a46-9f6f-4bc9-969c-af14027e341d","url":"https://stacklist.com/card/6cfe3a46-9f6f-4bc9-969c-af14027e341d","title":"LLM Context Window Sizes — Community Reference Table","source_url":"https://github.com/taylorwilsdon/llm-context-limits","note":"A community-maintained cheat sheet of context window and token limits for every major model — OpenAI, Anthropic, Google, Meta, Mistral, and more. Saves you hunting through multiple provider docs when you need to pick the right model for a long-context task.","image":{"url":"https://ucarecdn.com/cd84c2af-5f48-4d7e-8a7c-c56022637e27/","alt":"LLM Context Window Sizes — Community Reference Table","width":1200,"height":600},"stack":{"id":"e16dcdcb-06b9-481c-aef9-40b6d73a5c8e","title":"Master AI & LLM Context: From Basics to Production","url":"https://stacklist.com/c/technology/stack/e16dcdcb-06b9-481c-aef9-40b6d73a5c8e"},"created_at":"2026-07-02T10:08:45.071Z","updated_at":null,"aco":{"summary":"LLM Context Window Sizes Reference is a comprehensive guide documenting max context window lengths, input/output token limits, and feature compatibility for models from OpenAI, Anthropic, Qwen, Mistral, Deepseek, Llama, Phi, Gemini, and more. It covers API-driven model parameters including image/audio/video input support, tooling (MCP) support, and reasoning options, with practical tips for local deployment using Ollama and open-webui.","tags":["llm","context-window","token-limits","openai","api-reference","model-comparison","large-language-models"],"key_entities":[{"name":"OpenAI","type":"organization","confidence":0.99},{"name":"Anthropic","type":"organization","confidence":0.9},{"name":"Mistral","type":"organization","confidence":0.85},{"name":"Deepseek","type":"organization","confidence":0.85},{"name":"Google Gemini","type":"organization","confidence":0.8},{"name":"GPT-5","type":"technology","confidence":0.97},{"name":"GPT-4.1","type":"technology","confidence":0.95},{"name":"GPT-4o","type":"technology","confidence":0.93},{"name":"Ollama","type":"technology","confidence":0.92},{"name":"open-webui","type":"technology","confidence":0.88},{"name":"context window","type":"concept","confidence":0.98},{"name":"token limits","type":"concept","confidence":0.96},{"name":"KV cache","type":"concept","confidence":0.9},{"name":"flash attention","type":"concept","confidence":0.88},{"name":"MCP","type":"technology","confidence":0.85},{"name":"Qwen","type":"technology","confidence":0.85},{"name":"Llama","type":"technology","confidence":0.85},{"name":"Phi","type":"technology","confidence":0.8}],"classification":"reference","language":"en","confidence":0.85,"provenance":{"model":"claude-opus-4-6","tool":"@stacklist/mcp-server@2.0.0","confidence":0.85,"timestamp":"2026-07-02T10:08:56.923Z"},"token_counts":{"approximate":2224,"cl100k":2659},"content_hash":"sha256:60938075e9f7152864cc3a378c6a110daad4bbd52e7438435de31d227d8d087a","acp_version":"0.2","body_available":true,"body_tokens":2224,"visibility":"public","agent_accessible":true,"status":"final"},"_links":{"self":"/api/public/card/6cfe3a46-9f6f-4bc9-969c-af14027e341d.json","html":"https://stacklist.com/card/6cfe3a46-9f6f-4bc9-969c-af14027e341d","md":"/api/public/card/6cfe3a46-9f6f-4bc9-969c-af14027e341d.md","stack_json":"/api/public/stack/e16dcdcb-06b9-481c-aef9-40b6d73a5c8e.json"}}