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LLM token costs vary widely by language and data type

A new analysis reveals significant variations in token costs across different languages and data types when using large language models. The study found that Spanish text can cost up to 30% more than English on GPT-5, a substantial improvement from GPT-4. Claude's Opus model incurs approximately 2.5 times the cost per English word compared to its Sonnet model, despite a smaller sticker price difference. Notably, CSV data proved to be the most expensive format, with significantly more tokens per character than English prose, while code tokenization saw no improvement with GPT-5's new tokenizer. AI

IMPACT Understanding token costs is crucial for optimizing LLM usage and managing expenses, especially for multilingual applications and structured data processing.

RANK_REASON The cluster contains a detailed analysis and methodology for measuring LLM token costs across languages and data types, akin to a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

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COVERAGE [1]

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

    Tokens per Word: GPT-5 vs Claude vs GPT-4, Measured Across 7 Languages

    <p>Most token-cost guides repeat the same rule of thumb: one token is about three quarters of an English word. That figure is roughly right for English on a modern tokenizer, and increasingly wrong for everything else. Published numbers are surprisingly thin, so we measured it.</…