# Context Window

> The maximum amount of text (measured in tokens) a language model can consider at once — its prompt plus everything it has generated so far.

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- **term:** Context Window
- **category:** knowledge-memory
- **short_def:** The maximum amount of text (measured in tokens) a language model can consider at once — its prompt plus everything it has generated so far.
- **long_def:** The context window is the model's working memory: everything the model can 'see' for a single response — system prompt, retrieved documents, conversation history and tools — must fit inside it. Larger windows let an agent hold more context, but cost and latency rise with how much of the window is used.
- **see_also:** token-economics, tokenization, context-engineering
- **etymology_origin:** — (verify-against-primary-at-build)
- **related_to:** token-economics, tokenization, context-engineering, prompt-caching
- **contrast_with:** Unlike a database that stores unlimited data, the context window is a fixed per-request budget — anything beyond it must be retrieved, summarized or dropped.
- **example:** A model with a 1M-token context window can read a large codebase in one request; a 200K window forces an agent to retrieve only the relevant files.
- **source:** https://en.wikipedia.org/wiki/Large_language_model
- **status:** active
- **why_it_matters:** The context window sets the Cost of Retrieval an agent pays: the cheaper it is to fit your content in-window (markdown twins, concise structured data), the more of it an agent can afford to read.
- **sameAs:** https://en.wikipedia.org/wiki/Large_language_model
- **bridge_entity:** context-engineering
- **last_verified:** 2026-07-06
- **md_twin:** /glossary/context-window.md