How many tokens is one word?

For ordinary English prose, roughly 1 word equals 1.3-1.4 tokens, or about 0.75 words per token. The exact ratio depends on the model's tokenizer and the kind of text.

Why does code give a different number?

Code has lots of symbols, punctuation, and indentation that split into separate tokens, so the same word count produces more tokens than prose. Mixed content sits in between.

Why do models differ?

Each model uses a different tokenizer and vocabulary size. Llama 3's large vocabulary packs more text per token than GPT or Claude, so its token count for the same words is slightly lower.

It is a calibrated estimate, typically within 5-10% for English. For billing-critical or near-limit decisions, verify with the model's own tokenizer.

Words-to-Tokens Converter

Words-to-tokens converter

LLMs bill and budget by tokens, but humans think in words. This converter bridges the gap: enter a word count (or paste text) and instantly see the estimated token count for GPT-4o, Claude, Gemini, and Llama, adjusted for whether your content is prose, code, or a mix.

How it works

There is no universal words-to-tokens ratio because each model uses a different tokenizer. For English prose, one word is roughly 1.3-1.4 tokens. Code runs higher — closer to 2 tokens per word — because symbols, brackets, and indentation each split off as separate tokens. This tool applies a per-model, per-content-type ratio to your word count, so the estimate tracks the real tokenizer instead of using a single blanket number.

Tips and notes

Use the prose ratio for documents, emails, and chat; switch to code for source files and JSON; pick mixed for technical writing that interleaves the two. Remember that token estimates carry 5-10% error, so leave margin when you are sizing against a context window or a budget. To go the other direction — character counts to tokens — use the characters-to-tokens converter, which adds language modifiers for non-Latin scripts and emoji.