Tokens to Words Converter
Estimate words and characters from a token count, or tokens from words, with the 0.75 words per token rule and built-in context window presets.
Frequently Asked Questions about the Tokens to Words Converter
How many words is a token?
For common English text, one token is roughly 0.75 words, so 100 tokens is about 75 words. This converter multiplies your token count by 0.75 to estimate words and by 4 to estimate characters. Both ratios come from OpenAI's tokenizer guidance and are rules of thumb, not exact figures.
How do I convert words to tokens?
Switch the converter to the words to tokens direction and enter your word count. It divides words by 0.75, since one token is about 0.75 words, so 750 words works out to roughly 1,000 tokens. This helps you check whether a draft will fit inside a model's token budget.
How many characters are in a token?
On average about 4 characters of English text per token, so the converter estimates characters as tokens times 4. A 1,000 token passage is therefore around 4,000 characters. Short or punctuation heavy text can land outside this average.
How many words fit in a context window?
Pick a context window from the presets or type its token size, and the converter shows the matching word estimate. For example, an 8,192 token window is about 6,144 words, and a 128,000 token window is about 96,000 words. Remember that your prompt and the model's reply share the same window, so the usable space for either one is smaller.
How accurate is this estimate?
Treat the result as a planning estimate, not an exact count. The true number depends on the specific tokenizer, such as tiktoken's cl100k_base or o200k_base, plus spacing, punctuation, and rare words. For an exact figure, run your text through the tokenizer that matches your model.
Does it work for other languages or code?
The 0.75 words per token and 4 characters per token ratios are tuned for everyday English prose. Other languages, especially ones not written in the Latin alphabet, and source code often use more tokens per word, so the estimate will run low for them. Use it as a rough guide and verify with a real tokenizer when the language or content type differs.
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