Due to Byte-Pair Encoding (BPE) character splits, Chinese (Simplified) text generates 1.45× more tokens than equivalent English text. 1,000 Chinese (Simplified) words consume ~1,933 tokens in Microsoft Phi-3.5 MoE.
Relative to English baseline (1.0×).
Price to send 1,000 words of Chinese (Simplified) text into context.
Discounted price for repeated Chinese (Simplified) system context.
| Word Count Scale | Chinese (Simplified) Tokens | Chinese (Simplified) Input Cost | English Equivalent Cost | Tokenization Penalty |
|---|---|---|---|---|
| 1,000 words (Short Article) | 1,933 | $0.00029 | $0.0002 | +$0.000090 |
| 10,000 words (Whitepaper / Report) | 19,329 | $0.002899 | $0.002 | +$0.0009 |
| 50,000 words (Book / Corpus) | 96,643 | $0.0145 | $0.009998 | +$0.004499 |
| 100,000 words (Enterprise Repository) | 193,285 | $0.029 | $0.02 | +$0.008998 |
Rare characters and idioms still decompose into multi-byte UTF-8 token sequences.
Models like DeepSeek V4 and GLM 5.2 are natively optimized with extensive Chinese vocabularies for maximum cost efficiency.
Most LLM tokenizers are primarily trained on English-heavy web datasets. Non-Latin characters in Chinese (Simplified) (Hanzi) split across multiple Byte-Pair Encoding (BPE) sub-word or multi-byte UTF-8 tokens, requiring approximately 1.45× more tokens to encode the exact same semantic meaning as English.
Models like DeepSeek V4 and GLM 5.2 are natively optimized with extensive Chinese vocabularies for maximum cost efficiency. In addition, enabling prompt caching on static Chinese (Simplified) instructions or documentation saves 75–90% on input token rates.
Yes. Microsoft Phi-3.5 MoE has strong multilingual comprehension and generation capabilities in Chinese (Simplified) (简体中文). The difference is purely computational and financial due to sub-word token splits.