AI Token Estimator
AI models don't bill by the word or the character — they bill by the token. This free AI token estimator counts your text's characters and words, then estimates its token count for GPT and Claude-style models using each provider's published rule of thumb. Optionally, enter your model's current per-million-token price to convert the estimate into dollars. Everything runs 100% in your browser, so prompts stay private.
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100% client-side — your data never leaves this browser.
What a token is
Before a language model reads your text, a tokenizer chops it into tokens — chunks that average about three-quarters of an English word. Common words often survive as a single token ("the", "cat"); rarer words get split ("tokenization" → "token" + "ization"); and unusual strings shatter into many small pieces. Two numbers follow from this everywhere in AI work:
| Where tokens matter | Why |
|---|---|
| Context window | Models accept a fixed token budget per request (input + output combined) |
| Pricing | Providers bill per token, with output tokens usually costing more than input |
The published rules of thumb this tool uses: roughly 1 token per 4 characters of English for GPT-class models, and roughly 1 token per 3.5 characters for Claude — or about ¾ of a word per token.
Common use cases
- Budgeting API features — estimate what a prompt-heavy feature costs per user before building it.
- Context-window planning — check whether a document plus instructions fits the model's limit.
- Prompt slimming — see which instructions actually move the token needle when trimming prompts.
- Comparing model costs — same text, different model classes, different token counts and prices.
- Quoting client work — ballpark the AI cost component of a project proposal.
How to use it
- Pick the model class closest to what you'll actually call.
- Paste your prompt or document — character, word, and token counts update live.
- Optionally enter your model's current price per million tokens to see an estimated dollar cost.
- Remember the result is an estimate: verify against the provider's tokenizer before committing to tight budgets.
Tokens are not words
The word-to-token ratio surprises almost everyone the first time. In English prose, 100 words ≈ 130 tokens is typical — but the ratio swings wildly by content type. Minified JSON, with its dense punctuation, can approach one token per two characters. Source code lands somewhere in between, with identifiers splitting unpredictably ("getElementById" is several tokens). And the real outlier is script: languages written in non-Latin scripts — Chinese, Arabic, Thai — routinely cost 2–3× the tokens per character that English does, because the tokenizer's vocabulary was built around English-heavy training data. If your users write in multiple languages, estimate per language, not per character count.
Estimation limits
Be clear-eyed about what a heuristic can't do. Real tokenizers (byte-pair encoding and its variants) merge characters based on frequencies learned from training data — no character-count formula reproduces that exactly. This tool will be closest on plain English prose (usually within 10–20%) and furthest on code, non-Latin scripts, and strings heavy with symbols or emoji. It also counts only your input: the model's reply costs output tokens too, at the higher output rate. Use this for planning and comparison; for billing-grade precision, run the text through the provider's own tokenizer.
Frequently asked questions
- What is a token?
- A token is the unit an AI model reads and writes in — roughly a word fragment. "Tokenization" chops text into these pieces before the model sees it. Providers bill per token and limit context windows in tokens, so token counts drive both cost and capacity planning.
- How accurate is this estimate?
- It's a heuristic, good for budgeting and rough sizing — usually within 10–20% for plain English prose. For exact counts, use the model provider's own tokenizer tool. Treat this as a planning number, not a billing number.
- Why do different models count tokens differently?
- Each model family trains its own tokenizer with its own vocabulary. The same sentence becomes a different number of tokens under GPT-4o, GPT-3.5, or Claude tokenizers. That's why this tool lets you pick a model class — the per-character heuristic differs slightly between them.
- Do code and non-English text use more tokens?
- Yes, often significantly. Tokenizers are trained mostly on English prose, so code with lots of symbols, and languages with non-Latin scripts, get chopped into smaller pieces — more tokens per character. CJK text and emoji are especially token-hungry. The estimate here is calibrated for English; expect undercounting on other scripts.
- How do tokens relate to cost?
- Providers charge per million (or thousand) tokens, with separate rates for input and output — output is usually priced higher. Enter your model's current per-1M-token price above and this tool converts the estimate to dollars. Always check the provider's pricing page; rates change.
- What about the model's reply — does that cost tokens too?
- Yes. Every token the model generates counts as output tokens, billed at the (usually higher) output rate. When budgeting a feature, estimate both sides: your prompt going in and the expected response coming out.
- Is my text uploaded anywhere?
- No. Counting runs entirely in your browser — characters, words, and the token heuristic are computed locally. Paste prompts freely; nothing is sent to any server.