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Indian languages cost 4.4x more tokens than English for LLMs

By PulseAugur Editorial · [1 sources] ·

A recent analysis reveals that tokenizing Indian languages for large language models can be significantly more expensive than English, with some languages costing over four times as many tokens per sentence. This increased token count directly impacts GPU costs due to larger KV-caches, leading to reduced concurrency. The study highlights that the choice of tokenizer plays a crucial role, as different tokenizers yield vastly different cost multipliers for the same language. Romanizing Indian languages can reduce costs in some cases, but not universally, and the effectiveness depends on the specific tokenizer and transliteration scheme used. AI

IMPACT Increases operational costs for serving LLMs in non-English languages, highlighting the need for efficient tokenization strategies.

RANK_REASON Analysis of tokenization costs for Indian languages in LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

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

Indian languages cost 4.4x more tokens than English for LLMs

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Analysis of tokenization costs for Indian languages in LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Kaushikreddy Lingi ·

    A Hindi sentence costs 4.4x the tokens of English. Malayalam costs 7.3x.

    <p>If you serve an LLM to Indian-language users, your GPU bill has a hidden multiplier. I measured it instead of quoting it.</p> <h2> The measurement </h2> <p>I took the 1,012 FLORES devtest sentences, tokenized each language and its English counterpart with the Qwen3 tokenizer, …

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