Estimate Token Usage Before You Run It
Know how many tokens a job will consume before you send it — input plus an assumed response, costed per call and at scale.
A token is not a character. This shows the gap on a real prompt — characters, tokens, and the ratio between them — so the difference stops being abstract.
People reach for character count because it is easy, then get surprised by token bills. This loads a short prompt with no assumed response so the focus stays on the units: roughly four characters per token in English, but it swings with content and language. Tokens are the model's unit and characters are the keyboard's — counting one to predict the other is where estimates go wrong. The report makes the ratio visible instead of leaving you to guess it.
See both counts
Characters and tokens side by side on the same text.
Read the ratio
Roughly 4 characters per token in English — but only roughly.
Watch it move
Change the text type or language and the ratio shifts.
Roughly four characters per token in English, shown here rather than assumed. The report puts 'Characters: 274' next to 'Estimated tokens: ~70' and lists 'Tokens per character (approx): 0.3', making the gap concrete. The ESTIMATION NOTES define a token as a sub-word chunk, not a character, and warn the ratio swings with content type and language.
Because characters are the keyboard's unit and tokens are the model's, so one doesn't predict the other cleanly. This sample keeps 'Assumed response: none (input only)' to hold focus on the units, showing 274 characters map to about 70 tokens at a rough 4-to-1 that shifts with language and code. These remain estimates, not exact tokenizer output.
Know how many tokens a job will consume before you send it — input plus an assumed response, costed per call and at scale.
Paste a prompt, get an honest token estimate — a range, not a fake-precise number — plus the cost across GPT, Claude, and Gemini.
Turn a prompt into a dollar figure: input cost, output cost, combined per call, and the number that actually matters — cost per 1,000 calls.
Token budget planning for real workloads: how much of the window a transcript actually consumes, what is left for the answer, and how much headroom remains.
The "message too long" error has a structural fix: split at paragraph boundaries into sequenced chunks with wait rules, instead of pasting fragments and hoping.
A set of before-and-after examples showing exactly what prompt cleanup removes — and what it deliberately leaves alone.
Estimate how many tokens a prompt is and what it costs — honest ranges across GPT, Claude, and Gemini, with per-call and per-1,000-call pricing.
Counting a prompt's tokens before you send it tells you whether it fits the model, what it will cost, and whether the end might get cut off. Here's how to check and trim.