Multilingual token estimator
If you serve users in multiple languages, your per-request cost is not constant — the same message can cost two to four times more in Japanese or Hindi than in English, purely because of how the tokenizer splits the text. This estimator shows that gap across more than thirty languages so you can budget per market and decide where caching or model choice matters most.
How it works
Modern tokenizers use byte-pair encoding trained largely on English, so English is the most token-efficient language and others carry a multiplier. The tool takes your text, estimates its English-equivalent token count, and then applies each selected language’s empirical token-density multiplier to project how the same meaning would tokenize in that language. It ranks the results so the most expensive languages are obvious. Everything runs in your browser.
Why tokenization efficiency varies so much
BPE tokenizers build a vocabulary by starting with individual bytes and merging the most frequent pairs across the training corpus. Because the training data for most popular models skews heavily toward English, frequent English words and subwords earn their own vocabulary slots and tokenize into very few pieces. Characters from other scripts — Arabic, Thai, Devanagari, Chinese — were seen far less often in the vocabulary-building phase, so they get split into more pieces per character.
The result is a genuine cost asymmetry:
| Language family | Approximate token multiplier vs English |
|---|---|
| Other Latin-script European languages | 1.1 – 1.5× |
| Russian / Cyrillic | 1.5 – 2.0× |
| Arabic | 1.5 – 2.5× |
| Hindi (Devanagari) | 2.0 – 3.0× |
| Thai / Vietnamese | 1.5 – 2.5× |
| Japanese (mixed scripts) | 1.5 – 2.5× |
| Chinese (CJK characters) | 1.5 – 2.0× |
These are rough illustrative ranges. Actual multipliers depend on the exact tokenizer version and text content.
Practical pricing implications
Suppose you charge users a flat $0.01 per query and your LLM costs $1 per million tokens. For an average English query of 200 tokens that works fine — you spend $0.0002 and keep $0.0098. But a Hindi user asking the same question might generate 500 tokens, pushing your cost to $0.0005, which is still fine in isolation but represents 2.5× the compute cost per query. At scale, across tens of thousands of requests, the difference compounds.
Common responses to the token-cost gap:
- Language-aware pricing tiers — charge slightly more for high-multiplier markets, or offer fewer free queries.
- Model routing — use a more multilingual-efficient model for non-English markets where it is cost-effective.
- Prompt compression — translate only the necessary parts and keep instruction prefixes in English.
- Caching — cache common translated responses aggressively; reuse is worth more in high-multiplier markets.
For exact production numbers, run real translated strings through your provider’s tokenizer. This tool gives planning multipliers, not character-perfect counts.