Free tool
Words ↔ Tokens Converter
Convert between words, tokens, and 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.
Tokens
1,333
Words
1,000
Characters
5,333
How it fits common context windows
- 8K (older / small)17% used
- 32K4% used
- 128K (GPT-class)1% used
- 200K (Claude)1% used
- 1M (Gemini / long-context)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?
Frequently asked
How many tokens is 1,000 words?
Roughly 1,300 tokens for ordinary English (the standard rule of thumb is about 0.75 words per token, so words ÷ 0.75 ≈ tokens). 1,000 words is therefore ~1,333 tokens. Code and many non-English languages are denser — more tokens for the same word count — 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 (Claude) holds ~150,000 words; a 1M window (Gemini and other long-context models) 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're what you're billed on and what the context window is measured in. For English, 1 token ≈ ¾ of a word ≈ 4 characters, but it varies with punctuation, formatting, code, and language.
Is this exact?
No — it's 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.