Tokens in Bengali
How many tokens does Bengali text use, compared with English? We measured the same copy in both languages with OpenAI's two main tokenizers.
What the numbers say
The same website copy is 200 tokens in English and 309 tokens in Bengali on the newer o200k_base encoding, a ratio of 1.54×. On the older cl100k_base encoding the Bengali copy needs 998 tokens, which is 4.99× the English count of 200. At the same price per token, the same message costs about 1.54× as much in Bengali on the newer encoding. Among the 29 non-English languages we measured, Bengali ranks number 26 from the cheapest.
The newer encoding made a real difference: o200k_base used 69% fewer tokens than cl100k_base for Bengali, compared with 34% across all the non-English languages in the data. That is more than average, so Bengali gained more from the newer vocabulary than most.
Why Bengali? Bengali has its own script, with consonant clusters and vowel signs that combine into one visible syllable. Like other Indic scripts, each Unicode character is three bytes in UTF-8, and the script has historically had less representation in the text tokenizers learn from than Latin, so words are cut into many small pieces.
Indic scripts build a visible syllable out of several Unicode characters, and every one of those characters takes three bytes in UTF-8. Older tokenizers saw little text in these scripts, so they cut words into many small pieces. Newer vocabularies are larger and include more of these scripts, which is why the improvement from the older to the newer encoding is largest here.
How a Bengali sentence is split into tokens
This is the tagline of this site in Bengali. Each shaded block is one token (or a few tokens that together make one character) on o200k_base.
81 characters become 32 tokens on o200k_base and 93 tokens on cl100k_base. The English tagline, "Copy text on one device and paste it on another — no app, no account.", is 69 characters and 17 tokens on o200k_base and 17 on cl100k_base.
Want to see your own text? Paste it into the token calculator and compare the two tokenizers.
Bengali next to English and the closest languages
| Language | Characters | Tokens o200k_base | Tokens cl100k_base | × English o200k_base | × English cl100k_base |
|---|---|---|---|---|---|
| EnglishEnglish | 902 | 200 | 200 | 1.00× | 1.00× |
| Hindiहिन्दी | 910 | 298 | 878 | 1.49× | 4.39× |
| Thaiไทย | 773 | 301 | 710 | 1.50× | 3.55× |
| UkrainianУкраїнська | 967 | 304 | 514 | 1.52× | 2.57× |
| Bengaliবাংলা | 849 | 309 | 998 | 1.54× | 4.99× |
| Japanese日本語 | 474 | 313 | 421 | 1.56× | 2.10× |
See all 30 languages, with a chart and CSV and JSON downloads, on token cost by language.
What it means for your bill
API prices are per token, so the ratio is also a price ratio. If a prompt costs $1.00 in English on o200k_base, the equivalent Bengali prompt costs about $1.54, and about $4.99 on cl100k_base. A document of 1,000 Bengali characters is roughly 364 tokens on o200k_base. These are estimates from one sample: translated text is never exactly the same length as the original, and your text may be formal, technical or full of names, which changes the ratio.
To turn tokens into money, enter your provider's input and output prices per million tokens in the token calculator. For how pricing works, see how LLM API pricing works.
How to use fewer tokens with Bengali text
- Use a model with the newer tokenizer. For Bengali, o200k_base used 69% fewer tokens than cl100k_base in this sample, so a GPT-4o-class model needs fewer tokens than an older one for the same text.
- Count before you send. Paste a typical message into the token calculator to see the real count, then multiply by the number of requests.
- Keep fixed instructions short. A system prompt that is sent with every request is paid for every time. Writing the standing instructions in English is often cheaper per token than in Bengali; test that answers stay as good as you need.
- Cut what is not needed. Remove greetings, repeated context and long examples. The guide to reducing token usage has more.
- Be careful with emoji, rare characters and unusual formatting. They are often split into several byte-level tokens each.
Frequently asked questions
How many tokens is Bengali text?
In our measurement, the same website copy that is 200 tokens in English (902 characters) is 309 tokens in Bengali on o200k_base (849 characters) and 998 tokens on cl100k_base. As a rule of thumb, 1,000 characters of Bengali come to about 364 tokens on o200k_base and about 1176 on cl100k_base.
Is Bengali more expensive than English in GPT-4o?
Slightly to noticeably, depending on the text. On o200k_base, the encoding used by GPT-4o and newer OpenAI models, the Bengali copy needs 1.54× the English token count, so at the same price per token the same message costs about 1.54× as much. It was 4.99× on the older cl100k_base.
Which tokenizer is better for Bengali?
o200k_base needed 69% fewer tokens than cl100k_base for Bengali in this sample. Across all 29 non-English languages in the data, the average saving was 34%, so Bengali improved more than average.
Why does Bengali need more tokens than English?
Bengali has its own script, with consonant clusters and vowel signs that combine into one visible syllable. Like other Indic scripts, each Unicode character is three bytes in UTF-8, and the script has historically had less representation in the text tokenizers learn from than Latin, so words are cut into many small pieces. Indic scripts build a visible syllable out of several Unicode characters, and every one of those characters takes three bytes in UTF-8. Older tokenizers saw little text in these scripts, so they cut words into many small pieces. Newer vocabularies are larger and include more of these scripts, which is why the improvement from the older to the newer encoding is largest here.
Does this apply to Claude, Gemini or Llama?
Not directly. Other providers train their own tokenizers, so counts and ratios differ. These figures describe two OpenAI encodings only. Use your provider's own token counter before relying on a number for billing.
How was this measured?
We counted the tokens of the site's own copy in each language (tagline, two about paragraphs and four how-to steps) with the o200k_base and cl100k_base encodings from gpt-tokenizer 4.0.0, on 2026-10-12, and divided by the English count. It is one sample of marketing-style text, so other kinds of text, such as code or legal prose, will give different ratios.
Other languages and guides
Other languages: Japanese · Korean · Chinese (Simplified) · Chinese (Traditional) · Hindi · Thai · Russian · Vietnamese · Greek · Turkish · Indonesian · German · Spanish · Portuguese · French