Coalition for Language Equity in AI Research publishes study: non-English token costs are higher

Oct 30

1. 12:51

   Solidot

   Study Says Non-English Prompts Have Higher Token Costs

   A study published last month by the Coalition for Language Equity in AI Research found that AI models require far more tokens to process prompts in some languages than in English, which researchers call a hidden token tax. Among the 20 most popular languages, the most expensive language uses 1.5-5 times as many tokens as the cheapest language; when expanded to 200 languages, the token cost gap for one model increases by as much as 17 times. The study points out that a higher number of tokens means higher latency and lower effective context capacity, with the impact most severe in regions with limited computing and connectivity.

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2. 12:51

   SolidotRelated discussion

   Solidot joined this discussion ↗

   This post contributes to heat; it is not a formal report or confirmed fact.

3. 12:47

   IT Home · Artificial Intelligence

   New Study Says AI Model Token Consumption Varies by Up to 17 Times Depending on Language

   The Artificial Intelligence Language Fairness Research Alliance found that AI models vary dramatically in token consumption when processing different languages: among the 20 most commonly used languages on the internet, the difference is up to 1.5 to 5 times, and when expanded to 200 languages, one model's gap reaches as much as 17 times. The study calls this hidden extra cost the "tokenizer tax"—the more tokens consumed, the slower the AI response and the less context is available. Researchers recommend that AI labs improve next-generation tokenizer design and publish evaluation results for each language.

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4. 12:27

   Phoenix Technology

   New study says AI model token consumption varies by up to 17 times depending on language

   Research published last month by the AI Language Equity Research Alliance found that AI models consume vastly different numbers of tokens when processing different languages, with a gap of up to 17 times in one of the models. The study says token consumption already varies by 1.5 to 5 times among the 20 most commonly used languages on the internet, and the gap widens further when expanded to 200 languages; the researchers call this extra cost a hidden "tokenizer tax." Higher token consumption not only drives up costs but also slows response times and compresses the available context. The researchers recommend that AI labs publish evaluation results for each language and improve tokenizer design in the next generation.

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