Understanding Context Windows
A context window is the maximum number of tokens an AI model can process in a single API call. This includes your input (prompt, system message, documents) AND the model's output (response). If your total exceeds the limit, the request fails.
Why Context Window Size Matters
- RAG systems: Retrieval-augmented generation often feeds 10K-100K tokens of context. Not all models can handle this.
- Code generation: Large codebases or multi-file context can quickly consume 50K+ tokens.
- Document analysis: A 50-page document is roughly 25K-40K tokens. You need models with large windows.
- Multi-turn conversations: Chat history accumulates. Long conversations can hit 100K+ tokens.
Context Window Sizes by Provider (June 2026)
- 1M tokens: Claude Opus 4.8, Claude Sonnet 4.6, Gemini 3.1 Pro, Gemini 2.5 Pro, Gemini 2.0 Flash, DeepSeek V4 Pro/Flash, Llama 4 Scout/Maverick, Grok 4.3
- 272K tokens: GPT-5, GPT-5 mini, GPT-oss models
- 262K tokens: Mistral Large 3
- 256K tokens: Kimi K2.6, Jamba 1.7, Jamba 1.5
- 200K tokens: Claude 4 Opus (deprecated), Claude Sonnet 4 (deprecated)
- 128K tokens: GPT-4o, GPT-4o mini, Mistral Small/Medium, Llama 3.1 70B/8B, Command R+
Tips for Staying Within Limits
- Use
max_tokens to limit output length
- Implement conversation pruning for chat applications
- Chunk large documents and process in batches
- Use cheaper models for tasks that don't need full context
- Monitor token usage in production with logging