Why AI Models Like ChatGPT Are Far Less Capable in Non-English Languages

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Large language models such as ChatGPT and Claude appear fluent in many languages, but their underlying architecture creates significant performance gaps between English and other languages. The root cause lies in tokenization — the process of splitting text into units the model can process — which is heavily optimized for English due to its dominance in training data, sometimes comprising 95% of datasets like Llama 3's. This inefficiency means non-English text requires far more tokens to convey the same meaning, with some languages like Greek and Maltese needing up to 2.5 times more tokens per word than English. The consequences are threefold: higher API costs for non-English users, reduced effective context window capacity, and measurably lower task accuracy in underrepresented languages. Research, including the HRM8K benchmark on Korean, suggests the quality gap stems primarily from the model struggling to comprehend non-English input rather than from any fundamental limitation in its reasoning ability.

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