A recent analysis reveals that using languages other than English with most large language models incurs higher costs due to tokenization differences. Models like Claude Opus 5, for instance, tokenize Russian text at nearly three times the rate of English, leading to significantly higher expenses for non-English prompts, even when the per-token price is the same. This disparity stems from how tokenizers are trained on specific language corpora, with English sequences often assigned single tokens while other languages are broken into more fragments. AI
IMPACT Non-English users may face higher costs and reduced context window usage with current LLMs due to tokenization biases.
RANK_REASON Analysis of LLM tokenization costs for non-English languages.
- Anthropic
- Claude
- Claude Opus 5
- DeepSeek V3
- DeepSeek V4
- Gemini 3.7 Flash
- GigaChat 3
- GLM-5.3
- GPT-4
- GPT-5.x
- GPT-6
- Grok 4.7
- Kimi k3
- OpenAI
- Qwen 3
- YandexGPT 5 Lite
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