Due to Byte-Pair Encoding (BPE) character splits, Hindi text generates 2.4× more tokens than equivalent English text. 1,000 Hindi words consume ~3,199 tokens in Qwen3.8 27B (OpenRouter).
Quick answer
For 1,000 Hindi words, Qwen3.8 27B (OpenRouter) is modeled at 3,199 tokens—2.4× the English baseline. That is approximately $0.001344 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 Hindi text into context.
Discounted price for repeated Hindi system context.
| Word Count Scale | Hindi Tokens | Hindi Input Cost | English Equivalent Cost | Tokenization Penalty |
|---|---|---|---|---|
| 1,000 words (Short Article) | 3,199 | $0.001344 | $0.00056 | +$0.000784 |
| 10,000 words (Whitepaper / Report) | 31,992 | $0.0134 | $0.005599 | +$0.007838 |
| 50,000 words (Book / Corpus) | 159,960 | $0.0672 | $0.028 | +$0.0392 |
| 100,000 words (Enterprise Repository) | 319,920 | $0.1344 | $0.056 | +$0.0784 |
One Hindi word can expand into 3 to 6 tokens on legacy tokenizers.
Compare models with specialized Indic tokenization support or evaluate batch API processing for large Hindi corpus analysis.
Most LLM tokenizers are primarily trained on English-heavy web datasets. Non-Latin characters in Hindi (Devanagari) split across multiple Byte-Pair Encoding (BPE) sub-word or multi-byte UTF-8 tokens, requiring approximately 2.4× more tokens to encode the exact same semantic meaning as English.
Compare models with specialized Indic tokenization support or evaluate batch API processing for large Hindi corpus analysis. In addition, enabling prompt caching on static Hindi instructions or documentation saves 75–90% on input token rates.
Yes. Qwen3.8 27B (OpenRouter) has strong multilingual comprehension and generation capabilities in Hindi (हिन्दी). The difference is purely computational and financial due to sub-word token splits.