Context Window Visualizer
NewSee exactly how much of a model's context window your prompt consumes. Color-coded breakdown of system prompt, user messages, and assistant turns. Warns when you approach the effective quality limit (context rot).
Frequently Asked Questions
What is 'context rot'?
Context rot (also called lost-in-the-middle) is a phenomenon where LLMs degrade in quality before they reach their advertised context window limit. Research shows models perform best when critical information is near the beginning or end of the context, not the middle. The effective quality limit is typically 50–60% of the maximum context window.
How do I reduce my context window usage?
To reduce context: (1) Use a shorter system prompt — remove unnecessary instructions. (2) Summarize or truncate chat history instead of sending all turns. (3) Use RAG to send only the most relevant document chunks instead of full documents. (4) Choose a model with a larger context window for document-heavy tasks.
Why does context window size vary so much between models?
Context window size depends on the model's architecture, training data, and positional encoding method. Newer architectures like Gemini and GPT-4.1 use techniques that scale to millions of tokens, while older models are limited to 4K–32K. Larger context windows also increase latency and cost.