This free token and context window estimator tells you, in seconds, roughly how many tokens a piece of text is and whether it fits inside each major model context window. Paste a prompt, a document, or a whole transcript, and it shows a token estimate plus a fill bar and a clear fits or does not fit verdict for Claude, GPT and Gemini, alongside an approximate input cost. It uses a fast characters-per-token heuristic, so it is an estimate rather than an exact tokenizer, which is perfect for quickly sizing context before you build. Everything runs in your browser: no sign-up, no upload, nothing leaves your device.
The tool
About this tool
What is a token?
A token is the unit a language model actually reads and writes. It is not a word or a character but something in between: a common short word is often a single token, while a long or rare word is split into several. As a rough rule for English, one token is about four characters, and 1,000 tokens is roughly 750 words. Models bill per token and measure their limits in tokens, which is why estimating tokens, not words, is the right way to size a prompt or a document.
What is a context window?
The context window is the maximum number of tokens a model can hold in mind at once, counting both your input and the output it generates. If your text exceeds the window, the model cannot see all of it, and you have to trim, chunk, or summarise. In 2026 the windows are large: Claude Opus and Sonnet offer about one million tokens, Gemini models reach a million too, and others sit between two and four hundred thousand. The fill bars above show how much of each window your text would use, so you can see at a glance what fits.
Why this is an estimate, not an exact count
A real token count depends on the specific tokenizer each model family uses, and those differ between Claude, GPT and Gemini and even between versions. Bundling every tokenizer into a browser tool would be heavy and would still only match one model at a time. Instead this tool uses the widely cited four-characters-per-token heuristic, which is close enough to plan with for typical English prose but can be off for code, other languages, or text full of symbols and numbers. Treat the number as a reliable ballpark, and verify with the provider tokenizer when an exact count matters.
Using the estimate to plan cost and context
Once you know roughly how many tokens your text is, two decisions get easier. First, whether it fits: if a document is close to or over a model window, plan to chunk it, retrieve only the relevant parts, or pick a larger-window model. Second, what it costs: the approximate input cost shown next to each model is a starting point, and the full LLM Cost Calculator lets you add output tokens, monthly volume, caching and batch discounts for a complete estimate. For the deeper why, see our glossary entries on tokens and the context window and our context engineering guide.