Words to Tokens Converter
Estimate AI tokens from word count for prompts, drafts, API planning, and content budgeting.
Calculator
Token counts vary by model, language, punctuation, and tokenizer. This page uses a practical English estimate, not a guaranteed billing count.
Quick answer
estimated tokens = words x 1.33. Reverse formula: estimated words = tokens / 1.33.
- 750 words is about 1000 tokens
- 1500 words is about 2000 tokens
- 3000 words is about 4000 tokens
Quick conversion table
| Input | Result | Note |
|---|---|---|
| 100 words | tokens | estimated tokens = words x 1.33 |
| 250 words | tokens | estimated tokens = words x 1.33 |
| 500 words | tokens | estimated tokens = words x 1.33 |
| 1000 words | tokens | estimated tokens = words x 1.33 |
| 2000 words | tokens | estimated tokens = words x 1.33 |
| 5000 words | tokens | estimated tokens = words x 1.33 |
Formula explanation
estimated tokens = words x 1.33. estimated words = tokens / 1.33. Token counts vary by model, language, punctuation, and tokenizer. This page uses a practical English estimate, not a guaranteed billing count.
Precision note
Use this as a planning estimate. For exact billing or context limits, check the tokenizer for the specific model and language.
Background
AI models process text as tokens rather than words. A token can be a word, part of a word, punctuation, or spacing. English text is often estimated at about 0.75 words per token, or roughly 1.33 tokens per word.
Real-world use cases
- Prompt length planning
- API cost estimates
- Article and transcript sizing
- Context-window checks
Common mistakes to avoid
- Do not treat token estimates as exact billing counts.
- Remember that code, punctuation, URLs, and non-English text can tokenize differently.
- Check the model-specific tokenizer for critical context-window or API-cost planning.
FAQ
How many tokens are in 1000 words?
A rough English estimate is about 1330 tokens, but exact counts depend on the tokenizer.
Are tokens the same as words?
No. Tokens can be words, word parts, punctuation, or spaces depending on the tokenizer.
Can this estimate API cost exactly?
No. It is useful for planning, but exact cost requires the actual model tokenizer and pricing.
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