AI Model Context Window Visualizer

A context window is the maximum amount of text (measured in tokens) an LLM can process in a single request — including both input (your prompt + conversation history) and output (the model's response). If your input exceeds the context window, the model will either refuse the request or truncate older content. Larger context windows allow processing of entire codebases, books, or long conversation threads.

Google's Gemini 1.5 Pro achieves a 1 million token context window (extendable to 2M in preview) through a sparse attention mechanism called MQA (Multi-Query Attention) and architectural improvements that reduce the quadratic complexity of standard attention. This enables processing ~750,000 words or roughly 700 pages of text in a single API call — useful for large document analysis, video processing, and entire repository comprehension.

Not necessarily. While a larger context window allows more input, models can suffer from 'lost in the middle' degradation where information in the middle of very long prompts receives less attention than text near the beginning or end. Research shows retrieval accuracy drops for many models with very long contexts. Always test your specific use case rather than assuming more context equals better performance.

The standard approximation is 1 token ≈ 4 characters for English text, or roughly 0.75 words per token. A page of typical English text (250 words) ≈ 333 tokens. For code, the ratio varies by language — Python and JavaScript are often denser per token. Use our OpenAI Token Counter tool for more precise estimates, or use the tiktoken library in Python for exact GPT counts.