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Convert between tokens, words, characters, pages and reading time.
The Token to Word Converter moves between tokens, words, characters, pages and reading time for English prose, code or Chinese, Japanese and Korean text, with the cost at your price per million tokens and a comparison against common context windows.
API prices, context windows and output limits are all quoted in tokens, while people think in words and pages. This converter moves between the two: enter an amount in tokens, words, characters, pages or minutes of reading and see every other unit at once.
The ratios depend on what the text is. English prose runs at roughly 0.75 words per token, or about four characters per token — so 1,000 tokens is about 750 words. Source code is denser, at about half a word per token, because symbols and indentation cost tokens of their own. Chinese, Japanese and Korean run close to one token per character. Choose the kind of text and the figures change to match.
Add a price per million tokens to see what that amount costs, and the bars show how it compares with common context windows from 8K to one million tokens — alongside familiar references such as a blog post, a report and a novel for scale.
Start from tokens, words, characters, pages or minutes of reading.
Prose, code and CJK text tokenize very differently, and each has its own ratio.
Enter a price per million tokens to see what the amount costs.
See how the amount compares with 8K, 32K, 128K, 200K and 1M windows.
A post, an email, a report and a novel give the numbers a sense of scale.
Type a number and choose its unit: tokens, words, characters, pages or minutes.
English prose, code, or Chinese, Japanese and Korean. The ratios differ a lot.
Every unit is shown together, with reading and speaking time.
Add a price per million tokens and compare against common context windows.
About 750 words of English prose. Code and non-English text come out lower, because they break into more tokens per word.
A single-spaced page of about 500 words is roughly 670 tokens of English text.
Each model family has its own tokenizer vocabulary. Newer tokenizers usually encode common words more efficiently, so the same text can cost a different number of tokens on different models.
Punctuation, brackets, operators and indentation are often tokens of their own, so a line of code produces far more tokens than a line of prose with the same number of characters.
No. It converts with average ratios, which is ideal for planning. For an exact count of a specific text, use a token counter or the provider token counting endpoint.
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