Token counter
Count tokens for AI models, with a real tokenizer
Runs in your browser. Nothing is uploaded.
How it works
AI models read text as tokens, which are often parts of words. English averages about 4 characters a token. This uses OpenAI’s real o200k_base tokenizer, so that count is exact. Most other companies don’t publish theirs, so their counts are marked as estimates. Your text stays in your browser and is never uploaded.
Text is split into tokens, which are often parts of words.
The tokenizer loads when you start typing. The share of a context window shows how much of a model’s limit your text would use.
Worked example. “hello world” is 2 tokens with the o200k_base tokenizer. English text averages about 4 characters a token, and other languages often use more tokens.
Examples
| Tokenizer | How we count |
|---|---|
| o200k_base (OpenAI) | Exact |
| Other models | ≈ estimate, about 4 characters a token |
What this tool doesn’t cover
- Counts for models other than OpenAI’s are estimates.
- Real requests add a few tokens for formatting.
Questions
Why do Hindi and other languages use more tokens?
Tokenizers learn from mostly English text, so other scripts split into more pieces.
What is a context window?
The most text a model can consider at once, measured in tokens.
Is this exact for every model?
No. It’s exact for o200k_base, and an estimate for others. We label which is which.
What is a token?
A piece of text that AI models read, often part of a word. In English, one token is about four characters.
How many tokens is 1,000 words?
About 1,300 in English, though it depends on the words and the model.
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Built by QuickToolz.
Sources: OpenAI tiktoken (opens in a new tab)
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