Measuring the Tokenization Premium: A Cost Audit for Underserved Language Communities
2026-08-10 • Computation and Language
Computation and LanguageComputers and Society
AI summaryⓘ
The authors studied how different languages require different amounts of tokens when processed by language models, which affects costs and performance. They tested tokenizers on Python coding content translated into several languages and found that languages like Bengali and Yoruba need many more tokens than English for the same content. This means users of some languages get less efficient use of the model's capacity and face higher costs. The authors suggest that tokenization fairness should be considered to help underserved language communities, especially in education.
Large Language ModelsTokenizationToken CountContext WindowAPI CostMultilingual NLPPython Debugging CorpusTokenization EquityLanguage ResourcesLow-resource Languages
Authors
Avijit Roy, Proma Roy, Hrishitva Patel
Abstract
Large language models are increasingly deployed as general-purpose educational and technical assistance systems, but their underlying infrastructure does not treat languages equally. One underexamined source of disparity is tokenization: semantically equivalent content can require substantially different token counts across languages, affecting API cost, latency, and usable context length before a model is invoked. We introduce the Tokenization Equity Audit (TEA), a reproducible benchmark for measuring tokenization premiums in technical tutoring content. TEA evaluates three widely used tokenizers, GPT-4o's o200k base, Qwen2.5-7B, and Mistral-7B, on a 120-item Python debugging corpus translated from English into Bengali, Hindi, Arabic, Tamil, and Yoruba. Bengali and Hindi serve as the primary validated cases, while the remaining languages provide exploratory cross-script and cross-family comparisons. Across this corpus, Bengali requires (1.56\times) as many GPT-4o tokens as English, reducing a nominal 128k-token context window to an effective 82k-token English-equivalent capacity for the same semantic content. With the Qwen2.5 and Mistral tokenizers, Bengali requires up to (4.5\times) the English token count. Yoruba, despite using the Latin script, exhibits the highest GPT-4o tokenization premium at (2.37\times), indicating that tokenization inequity cannot be explained by script family alone. These results demonstrate that tokenization can create measurable economic and functional barriers, highlighting the need to treat tokenization as an equity-relevant infrastructure layer for underserved language communities, particularly where educational systems depend on low-cost or offline-capable AI tools.