LLM token counter

Paste any text to estimate how many tokens it is, then see what it would cost to send as a prompt across the major models. Everything runs in your browser — nothing you paste is uploaded.

Your text

Token count is an estimate (~4 characters per token), usually within 10–20% of a model's real tokenizer. Prices in USD, reviewed .

Estimate

Tokens (est.)
0
~4 chars each
Words
0
whitespace split
Characters
0
including spaces
Output tokens
0
your estimate

Cost per call by model

Your text as input + expected output. Cheapest first.

Roughly four characters a token

For English prose, one token ≈ 4 characters ≈ 0.75 words. Code, other languages, and symbol-heavy text break that ratio, so treat this as a close estimate, not a hard count.

Watch the context window

Every model caps total tokens (prompt + output). If your document is bigger than the window, you'll need to trim, chunk, or pick a longer-context model — better to know before the call fails.

System prompts add up

A long system prompt is sent on every call. Paste it here to see its per-call cost — trimming a verbose system prompt is an easy, repeatable saving at scale.

About token counting

How accurate is this estimate?

It uses the standard ~4-characters-per-token rule, usually within 10–20% of a real tokenizer. Code, non-English text, and heavy symbols tokenize differently. Fine for budgeting; for hard limits, use the provider's official tokenizer.

Why do tokens matter?

You're billed per token, and each model caps total tokens (prompt + output). Token count drives both your cost and whether a prompt fits the context window.

Does input or output cost more?

Output is usually priced higher — often several times — because generation is more compute-intensive. This tool prices your text as input and adds your expected output for the full call cost.

Is my text uploaded anywhere?

No. Counting and pricing happen entirely in your browser. What you paste never leaves your device.

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