Free tool
Tokens ↔ Words Converter
Convert tokens to words, words to tokens, or either to characters, and see how much fits in each model's context window. Type any amount in any unit; the rest update instantly. For the exact token count of a specific piece of text, use the Token Counter.
English prose. 1,333 tokens, 1,000 words, 5,333 characters
Tokens
1,333
Words
1,000
Characters
5,333
How it fits common context windows
- 8K (under every tracked model)17% used
- 32K (under every tracked model)4% used
- 128K (under every tracked model)1% used
- 200K (the smallest tracked window)1% used
- 1M (30 of 40 tracked models)0% used
Rule of thumb: ~1 token ≈ ¾ of an English word ≈ 4 characters (denser for code and many non-English languages). This is an estimate: the exact count depends on the specific text and the model's tokenizer.
Need the exact count for specific text?
How many words is N tokens?
The counts people most often arrive with (round numbers) and the context-window sizes printed on model spec sheets. Pages assume 250 words to a paperback page.
| Tokens | Words (English) | Words (code / dense) | ≈ Pages |
|---|---|---|---|
| 1,000 | 750 | 500 | 3 |
| 10,000 | 7,500 | 5,000 | 30 |
| 32,000 (32K) | 24,000 | 16,000 | 96 |
| 100,000 (100K) | 75,000 | 50,000 | 300 |
| 128,000 (128K) | 96,000 | 64,000 | 384 |
| 200,000 (200K) | 150,000 | 100,000 | 600 |
| 1,000,000 (1M) | 750,000 | 500,000 | 3,000 |
English is estimated at 0.75 words per token, code and most non-English text at 0.5, the same ratios the converter above runs on, so the two cannot disagree. These are rules of thumb, not tokenizer output. For the real count of a specific piece of text, the Token Counter runs the actual GPT tokenizer in your browser, and the Context Window Calculator answers the follow-up question of which models can hold it.
Frequently asked
How many words is 1 million tokens?
About 750,000 words of ordinary English, roughly 3,000 paperback pages, or four or five full-length novels. The standard rule of thumb is ~0.75 words per token, so tokens × 0.75 ≈ words. Code and most non-English text are denser, meaning the same million tokens carries closer to 500,000 words.
How many words is 100,000 tokens?
About 75,000 words of English, roughly 300 paperback pages, or one complete novel. A 128,000-token window holds about 96,000 words, and a 200,000-token window about 150,000. Both are small by 2026 standards: 128K is under every tracked model, and 200K is the smallest tracked window.
How many tokens is 1,000 words?
About 1,333 tokens for ordinary English: the standard rule of thumb is 0.75 words per token, so words ÷ 0.75 ≈ tokens. Code and many non-English languages are denser, 2,000 tokens for the same thousand words, so use the "Code / dense" setting above for those. For an exact figure on specific text, use the Token Counter.
How many words fit in a 128K context window?
About 96,000 words of English (128,000 tokens × ~0.75 words per token). A 200K window holds ~150,000 words; a 1M window, which 30 of the 40 models with a published window now reach, holds ~750,000 words, roughly a 3,000-page book. The converter above shows the fit for each common window as you type.
What's the difference between words, tokens, and characters?
Characters are individual letters/symbols. Words are space-separated. Tokens are the chunks a language model actually reads, usually a short word or a word-piece, and they are what you are billed on and what the context window is measured in. For English, 1 token is roughly 0.75 of a word, or about 4 characters, but it varies with punctuation, formatting, code, and language.
Is this exact?
No; it is an estimate based on the widely-used ratio (~0.75 words/token for English). The true count depends on the exact text and each model family's tokenizer (GPT, Claude, and Gemini differ slightly). It's accurate enough for planning prompt size, context fit, and rough cost. When you need the precise number for a specific piece of text, paste it into the Token Counter, which runs the real GPT tokenizer in your browser.