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 DeepSeek V4 Flash Vision Exp.
Quick answer
For 1,000 Japanese words, DeepSeek V4 Flash Vision Exp is modeled at 2,466 tokens—1.85× the English baseline. That is approximately $0.001085 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 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 | $0.001085 | $0.000587 | +$0.000499 |
| 10,000 words (Whitepaper / Report) | 24,661 | $0.0109 | $0.005865 | +$0.004986 |
| 50,000 words (Book / Corpus) | 123,303 | $0.0543 | $0.0293 | +$0.0249 |
| 100,000 words (Enterprise Repository) | 246,605 | $0.1085 | $0.0587 | +$0.0499 |
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. DeepSeek V4 Flash Vision Exp 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.