LLM context window comparison
Paste a prompt or document and see how many tokens it is, instantly — then check it against every major model's context window at once. Fill bars and a fits / too-big verdict show exactly how much room you have, with the model list loaded fresh from OpenRouter.
≈ 0 tokens
≈ 0 words · ≈ 0 pages
| Model | Your text | % used | Fit | |
|---|---|---|---|---|
Token counts use the GPT tokenizer family — other models' tokenizers differ by roughly ±10–20%. Context windows are what providers list on OpenRouter today, fetched live in your browser.
What a context window actually is
A context window is the maximum number of tokens a model can consider in one request — your prompt, documents, conversation history, and the response all share it. Models read tokens, not words: a token is roughly ¾ of an English word, so 1,000 tokens ≈ 750 words ≈ a page and a half. That's why "how many tokens is this document?" is the first question to answer before picking a model — a 128K-token window holds about 96,000 words, or roughly 190 pages, while an 8K window fits closer to 12.
Fitting isn't the whole story, though. Models degrade on very long inputs — recall of details buried in the middle of a stuffed window is measurably worse than near the start or end — and every input token is billed, so a maxed-out window makes each request dramatically more expensive. Use this context window calculator to find models with comfortable headroom rather than a bare fit, then put your token count through the LLM cost calculator to see what that prompt size costs per request and per month.
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