Models ranked by their ability to process massive documents, entire codebases, and extended conversations. Context windows now reach 1.0M tokens—equivalent to 1,333 pages of text.
A context window is the total amount of text an AI model can "see" at once—including your prompt and its response. Think of it as the model's working memory.
Larger context windows enable models to process entire books, analyze complete codebases, or maintain coherent conversations over hours without losing track of earlier details.
Analyze entire research papers, legal contracts, or books in a single pass
Understand project-wide code patterns without splitting files
Multi-hour conversations that maintain full context of earlier discussion
Skip complex retrieval systems—just load everything into context
| Rank | Model | Context | Quality | AA-LCR | License |
|---|---|---|---|---|---|
| 1 | GPT-5.2 (xhigh) OpenAI | 400K | 50.5 | 73% | Proprietary |
| 2 | GLM-5 (Reasoning) Z AI | 203K | 49.64 | - | Open |
| 3 | Claude Opus 4.5 (high) Anthropic | 200K | 49.1 | 74% | Proprietary |
| 4 | Gemini 3 Pro Preview (high) | 1.0M | 47.9 | 71% | Proprietary |
| 5 | GPT-5.1 (high) OpenAI | 400K | 47 | 75% | Proprietary |
| 6 | Kimi K2.5 (Reasoning) Kimi | 256K | 46.73 | - | Open |
| 7 | Gemini 3 Flash | 1.0M | 45.9 | 66% | Proprietary |
| 8 | Gemini 3 Flash Preview (Reasoning) | 1.0M | 45.9 | - | Proprietary |
| 9 | Claude 4.5 Sonnet Anthropic | 1.0M | 42.4 | 66% | Proprietary |
| 10 | MiniMax-M2.5 MiniMax | 205K | 41.97 | - | Open |
Use our interactive tool to compare pricing, response quality, and context handling for all 10 models.
As of January 2026, Gemini 3 Pro Preview (high) leads with a 1.0M token context window—equivalent to approximately 1,333 pages of text. This enables processing entire books, large codebases, or extremely long conversations in a single session.
Not necessarily. While larger context windows allow processing more text, the quality of reasoning and retrieval within that context matters more. Models are rated on AA-LCR (Long Context Retrieval) which tests whether models can find and use information from anywhere in their context window accurately.
Use long context when you need the model to understand relationships across an entire document or when dealing with under ~500 pages. Use RAG (Retrieval-Augmented Generation) for truly massive datasets, frequently changing information, or when you need citations to specific sources.