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Non-English prompts incur higher AI token costs due to language token tax

By PulseAugur Editorial · [1 sources] ·

AI models like GPT-4o and Gemini charge users based on tokens, not words, and the number of tokens required for a given text can vary significantly by language. Non-English prompts often require more tokens than English prompts due to how tokenizers are trained, leading to a "language token tax." While newer tokenizers have improved efficiency, differences persist, and users are advised to measure prompt costs across different models and languages. Additionally, prompt optimization techniques such as removing redundant rules, extra whitespace, and pretty-printing can reduce token usage and costs universally. AI

IMPACT Understanding tokenization differences across languages can help developers optimize costs and context window usage for global AI applications.

RANK_REASON The item discusses the pricing implications of tokenization across different languages for existing LLMs, rather than announcing a new model or research breakthrough.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Non-English prompts incur higher AI token costs due to language token tax

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The item discusses the pricing implications of tokenization across different languages for existing LLMs, rather than announcing a new model or research breakthrough.
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  1. dev.to — LLM tag TIER_1 English(EN) · Behrooz ·

    The language token tax: why your non-English prompts cost more, and what to do about it

    <p>AI APIs don't bill you per word. They bill you per <strong>token</strong>, and a token is whatever<br /> the model's tokenizer decided a piece of text should be.</p> <p>That sounds like a detail until you count. Here's one instruction, "Always answer in<br /> JSON format.", on…

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