AI Context Window Visualizer

How to use AI Context Window Visualizer

1. 1

   Enter your model's context window

   Input the model name (e.g., 'GPT') and its total context window (e.g., 128,000 tokens). The visualizer shows how the available context is allocated.

2. 2

   Add system prompt

   Paste your system prompt. The visualizer calculates tokens and shows how many tokens it consumes. Account for this when planning conversation length.

3. 3

   Estimate conversation length

   Input expected number of messages in the conversation, average tokens per user message, and average tokens per assistant response. The tool shows total conversation tokens and remaining context.

4. 4

   Plan for output

   Reserve tokens for the expected response (e.g., 1000-2000 for longer outputs). The visualizer shows the remaining context available. If it gets tight, reduce history or truncate earlier messages.

Questions and answers

How do LLM context window sizes compare in 2026?

The table lists the context window (maximum input) and maximum output tokens for the 39 current models in this site's vendor-verified dataset. One-million-token windows are now standard at the flagship tier across OpenAI, Anthropic, Google, xAI, and DeepSeek; mid-tier and open-weight models typically sit between 131K and 400K.

Source: official model documentation from OpenAI, Anthropic, Google, xAI, DeepSeek, Mistral, and Groq. Lineup verified 2026-10-03; refreshed October 3, 2026.

Does a bigger context window mean better results?

Not by itself. The advertised window is a capacity limit, not a quality guarantee — retrieval quality over very long prompts varies by model and by where information sits in the prompt. Some providers also bill long-context requests at a premium above an input-size threshold, so check the vendor's pricing page before assuming the full window costs the same per token. The practical question is usually allocation, not capacity: how much of the window your system prompt, tool definitions, conversation history, and retrieved documents each consume.

How should I budget a context window?

1. Measure the fixed overhead first: system prompt plus tool schemas. This is paid on every single request.
2. Reserve output headroom — the model's maximum output tokens come out of the same budget on most APIs.
3. Cap conversation history and retrieved-document chunks explicitly rather than letting them grow until requests fail.
4. Use the visualizer above to see the allocation as proportions of a real model's window — it runs client-side, so prompt structure and sizes stay on your machine.