A 128K context window is really 82K for Bengali, and up to 4.5x worse under two other tokenizers

The premium is not a pricing footnote, it is a third of the context window gone before the model reads a word

Tokenization cost is usually reported as a price multiplier: Bengali costs more per request because it takes more tokens to say the same thing. That framing treats the problem as a budget line. The 82,000-of-128,000 figure reframes it as a capacity problem: anything built around a fixed window, a long document, a multi-turn support chat, a full codebase pasted into a prompt, truncates roughly 36% sooner in Bengali than in English, for content of identical length and meaning, before the model has done anything.

The authors chose an applied task deliberately. Most tokenizer-fairness papers measure premiums on generic web text or Wikipedia, where premiums are already known to vary by script and morphology. This one runs the same 120 Python-debugging items, code plus natural-language explanation, through all three tokenizers in five languages, so the premium reported is specific to a task multilingual AI products actually ship: a coding tutor, a technical support agent, a debugging assistant. Our judgement: that choice is what makes the 82,000-token number usable by a product team, instead of a fact about Bengali text in the abstract.

What the paper measured, and when

"Measuring the Tokenization Premium: A Cost Audit for Underserved Language Communities," by Avijit Roy, Proma Roy and Hrishitva Patel, was submitted to arXiv on 10 August 2026 (arXiv:2608.09046v1, cs.CL) and accepted at an IJCAI 2026 workshop. The authors built a 120-item corpus of Python debugging tasks, code plus the natural-language explanation around it, in English, then translated it into Bengali, Hindi, Arabic, Tamil and Yoruba. Bengali and Hindi are the validated test languages; Arabic, Tamil and Yoruba are reported as exploratory. Each version was run through three tokenizers in production use: GPT-4o's o200k base, Qwen2.5-7B's tokenizer, and Mistral-7B's tokenizer, and the token count per language was compared to the English baseline on identical content.

The headline numbers, and what the abstract does and does not specify

All values taken directly from the arXiv:2608.09046v1 abstract, fetched 16 October 2026. Where a breakdown is not in the abstract, this table says so rather than estimating it.

Why a byte-pair tokenizer charges some scripts more, and why Latin-script Yoruba still loses

Script is a weak predictor on its own. Yoruba uses the same Latin letters GPT-4o's tokenizer was trained around, yet it scores a higher premium than Bengali, whose Brahmic script is visually and structurally farther from English. A byte-pair tokenizer's vocabulary is a frequency table built from its training mix, so a language is expensive whenever its sub-word patterns, whatever the script, were rare in that mix. That is why swapping tokenizers (from GPT-4o's o200k to Qwen2.5 or Mistral-7B) moves Bengali's premium from 1.56x past 4x: each vocabulary was built from a different corpus mix, so each one under-represents a different set of languages.

What this means for a team building or buying multilingual AI

A model comparison that only reports accuracy or BLEU-style quality scores is missing half the cost structure. Two products with identical per-token pricing and identical quality in Bengali are not equivalent if one uses a tokenizer that charges 1.56x and the other charges up to 4.5x for the same content: the second product's users get less usable context, pay more per conversation, and hit truncation sooner, for reasons invisible in a quality benchmark. For any product scoping multilingual support, API cost estimates and context-window sizing for non-English languages need the tokenizer-specific premium applied before launch, not discovered from a support ticket about truncated output.

The corpus choice also matters for anyone citing this kind of number. A premium measured on technical, code-adjacent text (the applied case here) will not automatically match a premium measured on conversational or legal text in the same language, because fragmentation depends on which sub-word patterns appear in the specific content, not only on the language.

Check it yourself

• Paper: arxiv.org/abs/2608.09046, "Measuring the Tokenization Premium: A Cost Audit for Underserved Language Communities," submitted 10 August 2026.
• Count tokens yourself for any string with OpenAI's public tokenizer (tiktoken, encoding o200k_base) and compare an English sentence against its Bengali translation.
• Qwen2.5 and Mistral-7B tokenizers are both open on Hugging Face (Qwen/Qwen2.5-7B, mistralai/Mistral-7B-v0.1); load each with AutoTokenizer and repeat the same comparison to see the premium move.

What would prove this wrong

If the paper's full text (beyond the abstract we verified) reports per-tokenizer Bengali premiums that do not reach 4.5x under either Qwen2.5 or Mistral-7B individually, the "up to 4.5x" framing would overstate the worst single-tokenizer case, since the abstract states it only as a combined ceiling. We flag this explicitly: as of 16 October 2026 we verified the abstract's exact wording but did not pull the full-text per-tokenizer breakdown table. A reader checking the HTML or PDF version who finds Qwen2.5 and Mistral-7B individually below 4.5x should treat this article's framing of "up to 4.5x" as a combined range, not a claim that either tokenizer alone reaches it.

Sources

1. Avijit Roy, Proma Roy, Hrishitva Patel, "Measuring the Tokenization Premium: A Cost Audit for Underserved Language Communities," arXiv:2608.09046, 10 August 2026. arxiv.org/abs/2608.09046