Context Window Visualizer

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 (July 2026)

• 1M tokens: Claude Opus 4.8, Claude Sonnet 4.6, Gemini 3.1 Pro, Gemini 2.5 Pro, Gemini 2.5 Flash-Lite, 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 3.3 70B, Llama 3.1 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