How can it count tokens without an API key?

It uses locally-bundled heuristics calibrated to each tokenizer family — characters per token and word-to-token ratios that closely match real tokenizers for English. There is no network call, so it works offline and never sees your API key.

How accurate are the estimates?

For ordinary English prose, expect within a few percent of the exact count. Accuracy drops for code, non-Latin scripts and heavy punctuation, where real tokenizers behave more variably. Use it for budgeting and context checks, not billing-exact figures.

Why do different families give different counts?

Each family trained its own tokenizer with a different vocabulary, so the same text splits into a different number of tokens. OpenAI and Claude are usually close; Gemini and Llama can diverge more, especially on code and symbols.

Is my text sent anywhere?

No. All estimation happens in your browser with no network request. Your text is never uploaded, stored or logged.

Token Count Estimator (No-Key, All Models)

Token count estimator — no key, all models

Need to know how many tokens your text will cost or whether it fits a context window — without pasting an API key into a random tool? This estimator uses locally-bundled tokenizer heuristics for every major model family and runs fully offline in your browser. No key, no upload, no network call.

How it works

Exact token counts require each provider’s tokenizer, but for English prose the relationship between characters and tokens is remarkably stable. The tool applies per-family calibration:

OpenAI (GPT / o-series) — ≈ 4 characters per token.
Claude — very close to OpenAI, slightly different on punctuation.
Gemini — a touch more tokens on the same text.
Llama — its own SentencePiece vocabulary, typically a few percent higher.

It blends a characters-per-token estimate with a words-per-token check so a wall of symbols or a list of short words doesn’t skew the result, then reports a token count for each selected family next to raw character and word counts.

Tips for accurate budgeting

Estimate in the same language you’ll use. Non-English and mixed scripts tokenize differently — re-check with representative text.
Treat code separately. Source code and JSON tokenize less predictably than prose; add a margin.
Add headroom for context limits. Leave room for the system prompt and the completion, not just the input you pasted.
Confirm before billing. For an exact figure, use the provider’s own tokenizer; this tool is for fast, key-free planning.