How are tokens estimated without a tokenizer library?

The counter uses well-established character-and-word heuristics calibrated per model family — roughly four characters per token for English, adjusted for punctuation and whitespace. It is an estimate, not the exact byte-pair count.

For ordinary English prose it is typically within about 10 to 15 percent of the real count. Code, non-Latin scripts and heavy punctuation tokenize less predictably, so treat those as rougher. Confirm with the provider's tokenizer for billing-critical work.

Why do the models show different counts?

Each provider uses a different tokenizer, so the same text splits into a slightly different number of tokens. The per-model figures reflect those known differences in average token density.

Is my prompt sent anywhere?

No. The estimate is computed entirely in your browser. Your prompt is never uploaded, logged or stored.

Prompt Token Counter

Estimate prompt tokens before you spend a call

Tokens drive both your cost and whether a prompt fits the model’s context window. This counter gives you a fast, per-model token estimate for GPT-4o, Claude and Gemini as you type, plus a rough input-cost figure and how much of a typical context window the prompt uses — so you can trim before you send rather than after a 400 or a surprise bill.

How it works

Counting exact tokens requires each provider’s byte-pair tokenizer, which is too heavy to ship into a browser page. Instead this tool uses a calibrated heuristic: it measures character count, word count and whitespace, then applies a per-model density factor reflecting how each tokenizer tends to split English text (around four characters per token, with adjustments). The result is a close estimate for ordinary prose. It then multiplies by published input prices to show an approximate cost and compares against a representative context-window size for each family.

Tips and notes

Use this for budgeting and context-fit checks, not for exact billing — the real number comes from the provider’s own tokenizer (tiktoken for OpenAI, the Anthropic token-count endpoint, Gemini’s count-tokens API). Estimates are least accurate on source code, JSON, non-English scripts and text with unusual punctuation, where token density diverges from prose. If a prompt sits right at a context limit, leave headroom for the model’s response, which also consumes tokens from the same budget. Everything runs locally, so pasting sensitive prompt text is safe.