AI language models don't see words. They split text into tokens, small pieces that can be a whole word, part of a word, a space or a punctuation mark. Context windows, rate limits and API prices are all measured in tokens, so it helps to know roughly how many you are using.
The rule of thumb
For typical English prose, 1,000 tokens is about 750 words, or 1 token is roughly 0.75 words. Another common approximation is about 4 characters per token.
750 words to tokens
750 words ÷ 0.75equals1,000 tokens
200,000 tokens to words
200,000 tokens × 0.75equals150,000 words
That second line is a useful sense of scale: a 200,000-token context window holds roughly 150,000 words of English text, around the length of a long novel.
Why words and tokens differ
Common short words like "the" or "and" are usually one token each. Longer or rarer words are split into several pieces. Numbers, code, URLs and unusual names can use a lot of tokens for their length.
When the rule of thumb breaks down
- Code: brackets, indentation and variable names often use more tokens per word than prose.
- Other languages: many languages use more tokens per word than English, sometimes considerably more.
- Data formats: JSON, tables and lists of numbers can be token-heavy.
- Different models: each provider uses its own tokenizer, so the same text can produce different counts.
When you need an exact count
For planning and rough cost estimates, the 0.75 rule is normally close enough. When you are near a context limit, or billing a client for usage, use the provider's own token counting tool or library for the exact number.
Tip: Remember that both directions count. Your prompt, any documents you include and the model's reply all use tokens, and replies are usually priced higher.