Tokka-Bench: Evaluating Tokenizers Across 100 Natural and 20 Programming Languages

Abstract:Large language models rely on subword tokenizers whose quality varies across languages, yet no standardized multi-metric framework exists for broad comparative evaluation. We introduce Tokka-Bench, an open-source framework that evaluates tokenizers on five complementary metrics -- bytes per token, unique token coverage, subword fertility, word-split rate, and vocabulary composition -- across 100 natural languages (30+ scripts) and 20 programming languages, using language-aware segmentation adapted to each writing system. Comparing seven BPE tokenizers (GPT-2, GPT-4, gpt-oss, Llama 3.1, Gemma 3, Qwen3, and Kimi K2) within individual languages, we find that vocabulary allocation strategy matters more than raw vocabulary size, and that programming-language efficiency has converged among recent tokenizers despite divergent natural-language profiles. The framework, data, and interactive dashboard are publicly available.

5 pages, 5 figures. Code and data: this https URL. Interactive dashboard: this https URL

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2610.08794 [cs.CL]

(or arXiv:2610.08794v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.08794 Focus to learn more arXiv-issued DOI via DataCite

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