Why non-English text costs more tokens: multilingual tokenization explained
Tokenization efficiency matters more for non-English applications. Benchmark actual token counts in your target language before choosing a model for cost reasons. Gemini and Claude often give more tokens-per-dollar for multilingual workloads despite higher per-token pricing.
A 100-word paragraph in English might use 130 tokens. The same paragraph translated to Chinese might use 300 tokens, Japanese 250, Hindi 350, and Arabic 280. This happens because most LLM tokenizers are trained predominantly on English text, so common English words become single tokens while words in other languages are split into multiple subword pieces.
By TechCompare · Updated
How this is calculated
The tokenization gap has real cost implications. A multilingual customer support chatbot that serves English, Spanish, Japanese, and Arabic users will pay 2-3x more per non-English interaction. Gemini and Claude tokenizers tend to handle non-English text more efficiently than GPT because they were trained on more balanced multilingual corpora. Llama's tokenizer is notably inefficient for Asian scripts because its training data skews toward English and Latin-script languages. If your application is primarily non-English, benchmark the actual token counts across models. The model with the lowest per-token price might actually cost more overall if its tokenizer produces 2x more tokens for your target language.
Verdict
The cost gap tracks vocabulary balance. GPT tokenizers trained mostly on English split non-English text into many subword fragments, so a 130-token English paragraph can balloon to 300 tokens in Chinese or 350 in Hindi. Gemini and Claude built more balanced multilingual vocabularies, which can make a higher per-token price actually cost less per conversation. Llama is notably weak on Asian scripts because its training skews toward English and Latin.
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Frequently asked questions
Why does Chinese text use more tokens than English?
How much more does a multilingual app cost in tokens?
Which LLM is most token-efficient for Asian languages?
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