Due to Byte-Pair Encoding (BPE) character splits, French text generates 1.18× more tokens than equivalent English text. 1,000 French words consume ~1,573 tokens in Hy-MT2-30B-A3B (OpenRouter).
Quick answer
For 1,000 French words, Hy-MT2-30B-A3B (OpenRouter) is modeled at 1,573 tokens—1.18× the English baseline. That is approximately $0.000116 as input, before any cache discount.
Method & trust
The language page applies the published language multiplier to a 1,333-token English baseline, then applies the model's input/output rates. Tokenizers differ, so benchmark representative text before production budgeting.
Relative to English baseline (1.0×).
Price to send 1,000 words of French text into context.
Discounted price for repeated French system context.
| Word Count Scale | French Tokens | French Input Cost | English Equivalent Cost | Tokenization Penalty |
|---|---|---|---|---|
| 1,000 words (Short Article) | 1,573 | $0.000116 | $0.000099 | +$0.000018 |
| 10,000 words (Whitepaper / Report) | 15,729 | $0.001164 | $0.000986 | +$0.000178 |
| 50,000 words (Book / Corpus) | 78,647 | $0.00582 | $0.004932 | +$0.000888 |
| 100,000 words (Enterprise Repository) | 157,294 | $0.0116 | $0.009864 | +$0.001776 |
Slightly longer average sentence length than English.
Mistral models (European-first architecture) have exceptionally clean French tokenization efficiencies.
Most LLM tokenizers are primarily trained on English-heavy web datasets. Non-Latin characters in French (Latin) split across multiple Byte-Pair Encoding (BPE) sub-word or multi-byte UTF-8 tokens, requiring approximately 1.18× more tokens to encode the exact same semantic meaning as English.
Mistral models (European-first architecture) have exceptionally clean French tokenization efficiencies. In addition, enabling prompt caching on static French instructions or documentation saves 75–90% on input token rates.
Yes. Hy-MT2-30B-A3B (OpenRouter) has strong multilingual comprehension and generation capabilities in French (Français). The difference is purely computational and financial due to sub-word token splits.