# Words, Characters, Pages and Tokens — Converter

> Convert between words, characters, pages and tokens using measured ratios for prose, code, JSON and CJK.

One English word averages about 1.33 tokens, so 1,000 words is roughly 1,330 tokens and 1,000 tokens is roughly 750 words. Source code runs about 2.4 tokens per word and JSON about 2.7, so structured data costs roughly twice as much as prose for the same page count. This converter applies measured ratios for each content type rather than a single average.

**URL:** https://convertto.tech/t/words-to-tokens-converter
**Category:** AI & LLM Tools (https://convertto.tech/c/ai-tools)
**Privacy:** Runs entirely in the browser; no upload
**Cost:** Free, no sign-up
**Last updated:** 2026-08-01

## Key facts

- **English prose:** 1 word ≈ 1.33 tokens · 1 token ≈ 4 characters
- **Source code:** 1 word ≈ 2.4 tokens · 1 token ≈ 2.6 characters
- **JSON:** 1 word ≈ 2.7 tokens · 1 token ≈ 2.6 characters
- **Privacy:** Runs entirely in your browser — nothing is uploaded
- **Cost:** Free, unlimited, no sign-up

## How to use

1. Set the amount.
2. Choose the from.
3. Choose the to.
4. Choose the content type.
5. The result appears immediately — copy or download it.

## FAQ

### How many tokens is 1,000 words?

About 1,330 tokens for ordinary English prose. For technical writing expect around 1,550, for source code around 2,400, and for JSON around 2,700 — the content type matters far more than which model you are using.

### Why does the same text cost more in French or Japanese?

The vocabularies are trained predominantly on English, so other languages get split into more, smaller pieces. French and German typically run 20–50% more tokens than the equivalent English, and CJK text can cost several times more per unit of meaning.

### Is this exact?

No — it is a ratio estimate, which is what you want for planning and budgeting. For an exact number, paste the actual text into the token counter, which runs the real vocabulary.

## Related tools

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- [LLM Model Comparison](https://convertto.tech/t/llm-model-comparison): Every current model side by side: price per million tokens, context window, max output, modalities and cache rates.
- [LLM Stream Parser](https://convertto.tech/t/llm-stream-parser): Paste a raw server-sent-event stream and get the reconstructed message, tool calls, usage and stop reason.
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