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Does CLEAR report that tokenizer design can make some languages up to ~17× more token-expensive?

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Verdict

Confirmed. CLEAR published its tokenizer-tax study on clear-languages.org reporting gaps up to about 17× across ~200 languages; DailyCited lists that post as Official on the Nov 2 brief.

Evidence timeline · 1 source · newest first

  1. Unconfirmed → Confirmed

  2. Confirmed

    CLEAR (Coalition for Language Equity in AI Research) reports that mainstream tokenizers optimize for English and high-resource languages, so the same sentence can cost far more tokens elsewhere (example cited: 13 vs 26 vs 78 tokens across English/Malayalam/Amharic), with gaps up to about 17× across ~200 languages. Primary CLEAR blog post was opened for this run.

    Source: Telegram @zaihuapd · Official story →
Quick answers
Has CLEAR confirmed it?
Yes. CLEAR confirmed it on Nov 1.
On DailyCited
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