Datasets:
rows list | diacritic_surcharge dict | n_sentences int64 | n_languages int64 | tokenizers list |
|---|---|---|---|---|
[{"tokens":128377,"chars":111639,"bytes":199902,"tokens_per_sentence":126.85474308300395,"bytes_per_(...TRUNCATED) | {"gpt2":{"marked_tokens":105095,"stripped_tokens":49133,"surcharge":2.138990088128142},"bloom":{"mar(...TRUNCATED) | 1,012 | 204 | ["gpt2","bloom","qwen2.5","nllb","xlm-r","mbert","aya","phi-3","mistral","falcon","gpt-3.5/4 (cl100k(...TRUNCATED) |
owóorí: the tokenizer tax, measured
Token-count premiums for the 204 languages of FLORES-200 under 12
tokenizers, from the identical 1,012 professionally translated sentences.
The premium is tokens(language) / tokens(English) on the same content, so
it reads directly as a price multiplier for API cost, latency and context
shrinkage.
- Interactive explorer: https://kenny0bi.github.io/owoori/
- Method, figures, code: https://github.com/Kenny0bi/owoori
Files
premiums.csv: one row per language x tokenizer (204 x 12 = 2448 rows): premium, raw token counts, characters, bytes, bytes per token.measurements.json: everything above plus the Yoruba diacritic-surcharge measurements (marked vs stripped text under every tokenizer).
Headlines
- Santali pays 12.74x English's rate at the frontier (median of cl100k, o200k, Qwen2.5), the worst of the 204.
- 61% of languages pay more than 2x at the frontier; 17% pay more than 4x.
- The tax is a design choice: Amharic pays 5.78x under GPT-4o and 1.29x under NLLB; Yoruba pays 2.17x and 1.48x.
- Writing Yoruba with its tone and dot-below marks costs 1.52x the stripped text under GPT-4o: a second tax, on orthography itself.
Provenance
Source text: FLORES-200 devtest (NLLB Team 2022, CC-BY-SA 4.0), from Meta's public distribution. Tokenizers: GPT-2, Phi-3, Mistral v0.3, Falcon-7B, cl100k_base, o200k_base, Qwen2.5, NLLB-200, XLM-R, mBERT, Aya-101, BLOOM, all loaded from their public repositories. Llama and Gemma are gated and not included. Measurement code and tests are in the GitHub repository.
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