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Dictionary

Context Window

The maximum amount of text (in tokens) an LLM can consider in a single call.

1 min readupdated 2026-06-21

/ quick answer

The Context Window is the hard limit on how much input + output a model can hold at once, measured in tokens. Larger windows allow longer documents and richer agent state, but cost more and can dilute attention — making retrieval and summarization still essential.

The maximum amount of text (in tokens) an LLM can consider in a single call. The Context Window is the hard limit on how much input + output a model can hold at once, measured in tokens. Larger windows allow longer documents and richer agent state, but cost more and can dilute attention — making retrieval and summarization still essential. In practice: A 1M-token model could read an entire codebase at once, but a focused 8K-token RAG pipeline answering one question is often cheaper, faster and more accurate. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
The Context Window is the hard limit on how much input + output a model can hold at once, measured in tokens. Larger windows allow longer documents and richer agent state, but cost more and can dilute attention — making retrieval and summarization still essential.
Example
A 1M-token model could read an entire codebase at once, but a focused 8K-token RAG pipeline answering one question is often cheaper, faster and more accurate.
Related Workflows
Related Tool Stacks
/ frequently asked

What is Context Window?

The Context Window is the hard limit on how much input + output a model can hold at once, measured in tokens. Larger windows allow longer documents and richer agent state, but cost more and can dilute attention — making retrieval and summarization still essential.

What is an example of Context Window?

A 1M-token model could read an entire codebase at once, but a focused 8K-token RAG pipeline answering one question is often cheaper, faster and more accurate.

Why does Context Window matter for AI and automation?

The maximum amount of text (in tokens) an LLM can consider in a single call. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.

/ topics#ai#models
↳ connected nodes
Workflow↳ linked
RAG Content Ingestion Pipeline
Convert messy docs into searchable, cited knowledge chunks for AI systems.
Tool Stack↳ linked
RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
Workflow↳ linked
Context Window Optimization Workflow
This workflow outlines steps to optimize the information fed into an LLM's finite context window, ensuring maximal relevance and efficiency while managing token limits.
Dictionary↳ linked
Contextual Compression
Contextual compression is a technique used to reduce the size of the input context for a Large Language Model (LLM) while retaining its most relevant information, typically by summarizing or filtering.
Workflow↳ linked
Multi-Turn Context Management Workflow
This workflow manages conversation history and other dynamic context in multi-turn interactions with LLMs, ensuring coherence and relevance over extended dialogues.
Dictionary↳ linked
Token Budgeting
Token budgeting is the strategic allocation and management of token usage within large language model (LLM) operations to control costs and optimize performance.
Workflow↳ linked
Reduce Agent Context Window Costs
This workflow details methods to minimize the token count within an AI agent's context window, directly reducing LLM API costs.
Dictionary↳ linked
Token Budget
A token budget is a predefined limit on the number of tokens an AI application or specific request can consume within a given period or for a single interaction. It is a critical mechanism for controlling costs and managing resource allocation for Large Language Model (LLM) usage.