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Indian languages face 'token tax' with AI models, research finds

By PulseAugur Editorial · [1 sources] ·

A new research paper, "The Indic Token Tax in 2026," highlights significant disparities in how different language models tokenize Indian languages compared to English. The study found that tokenizers for models like GPT-4o and GPT-4 require substantially more tokens for content in languages such as Odia and Santali, leading to increased costs and reduced efficiency. Sarvam-30B/105B is noted as the only third-party tokenizer that keeps token counts relatively low across all tested Indian languages. AI

IMPACT Highlights potential cost and efficiency barriers for non-English languages in AI applications, necessitating better tokenizer development.

RANK_REASON The cluster contains a research paper published on arXiv detailing findings about AI model tokenization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

Indian languages face 'token tax' with AI models, research finds

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The cluster contains a research paper published on arXiv detailing findings about AI model tokenization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Johin Johny Arimbur, Ankur Ashit Ghosh, Shervin Sam ·

    The Indic Token Tax in 2026: Measuring Tokenizer. Disparity and Its Cost for the Languages of India

    arXiv:2611.13058v1 Announce Type: cross Abstract: Commercial language model APIs bill per token, so a tokenizer that needs more tokens for the same content in one language makes that language more expensive, slower, and able to fit less text in a fixed context window. We measure …

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