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 Text Embedding 3 (Large).
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 | $0.000204 | $0.000173 | +$0.000031 |
| 10,000 words (Whitepaper / Report) | 15,729 | $0.002045 | $0.001733 | +$0.000312 |
| 50,000 words (Book / Corpus) | 78,647 | $0.0102 | $0.008665 | +$0.00156 |
| 100,000 words (Enterprise Repository) | 157,294 | $0.0204 | $0.0173 | +$0.003119 |
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. Text Embedding 3 (Large) has strong multilingual comprehension and generation capabilities in French (Français). The difference is purely computational and financial due to sub-word token splits.