Not all languages are equal for AI

A counter-intuitive but consequential finding: writing your prompts in English is often cheaper, faster, and slightly more accurate than writing them in French — even when the final output must be in French. This article explains why, and when it matters.

Tokens, not words

Models do not see words. They see tokens — sub-word fragments produced by a tokenizer trained mostly on English text. For English, the tokenizer is efficient: roughly one token per four characters. For French, German, or Spanish, that ratio is worse. For Mandarin or Korean, worse still.

[Migration in progress — full article body to be brought across from the original Notion source.]

What to do about it

• For internal prompts, work in English when you can.
• For client-facing output, write in French (or whatever target language).
• When cost matters, run the prompt in English and translate the result.
• When nuance matters, write in the target language and accept the surcharge.