Due to Byte-Pair Encoding (BPE) character splits, Japanese text generates 1.85× more tokens than equivalent English text. 1,000 Japanese words consume ~2,466 tokens in Cohere Command R.
Relative to English baseline (1.0×).
Price to send 1,000 words of Japanese text into context.
Discounted price for repeated Japanese system context.
| Word Count Scale | Japanese Tokens | Japanese Input Cost | English Equivalent Cost | Tokenization Penalty |
|---|---|---|---|---|
| 1,000 words (Short Article) | 2,466 | $0.00037 | $0.0002 | +$0.00017 |
| 10,000 words (Whitepaper / Report) | 24,661 | $0.003699 | $0.002 | +$0.0017 |
| 50,000 words (Book / Corpus) | 123,303 | $0.0185 | $0.009998 | +$0.008498 |
| 100,000 words (Enterprise Repository) | 246,605 | $0.037 | $0.02 | +$0.017 |
Older tokenizers like cl100k_base split Kanji characters into 2-3 tokens each, almost doubling API bills compared to English.
Use modern models with 200k+ vocabularies (like GPT-5.6 o200k_base or Gemini 3.x) which compress Japanese significantly more densely.
Most LLM tokenizers are primarily trained on English-heavy web datasets. Non-Latin characters in Japanese (Kanji / Hiragana / Katakana) split across multiple Byte-Pair Encoding (BPE) sub-word or multi-byte UTF-8 tokens, requiring approximately 1.85× more tokens to encode the exact same semantic meaning as English.
Use modern models with 200k+ vocabularies (like GPT-5.6 o200k_base or Gemini 3.x) which compress Japanese significantly more densely. In addition, enabling prompt caching on static Japanese instructions or documentation saves 75–90% on input token rates.
Yes. Cohere Command R has strong multilingual comprehension and generation capabilities in Japanese (日本語). The difference is purely computational and financial due to sub-word token splits.