Tokens in Vietnamese
How many tokens does Vietnamese 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 239 tokens in Vietnamese on the newer o200k_base encoding, a ratio of 1.20×. On the older cl100k_base encoding the Vietnamese copy needs 391 tokens, which is 1.96× the English count of 200. So on the newer encoding Vietnamese costs only a modest amount more than English. Among the 29 non-English languages we measured, Vietnamese ranks number 8 from the cheapest.
The newer encoding made a real difference: o200k_base used 39% fewer tokens than cl100k_base for Vietnamese, compared with 34% across all the non-English languages in the data. That is more than average, so Vietnamese gained more from the newer vocabulary than most.
Why Vietnamese? Vietnamese uses the Latin alphabet with many accents and tone marks, and some letters carry two marks at once. Each word is short and separated by spaces, but accented letters are multi-byte and the accented forms are rarer in training text, so words can split at the accent.
Vietnamese is written in Latin letters, so its basic characters are familiar to every tokenizer, but the many accents and tone marks create characters that are multi-byte and less common. That is why its cost is higher than the other Latin-script languages even though the alphabet looks similar.
How a Vietnamese sentence is split into tokens
This is the tagline of this site in Vietnamese. Each shaded block is one token (or a few tokens that together make one character) on o200k_base.
103 characters become 28 tokens on o200k_base and 46 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.
Vietnamese 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× |
| ItalianItaliano | 918 | 231 | 264 | 1.16× | 1.32× |
| MalayBahasa Melayu | 991 | 235 | 288 | 1.18× | 1.44× |
| VietnameseTiếng Việt | 867 | 239 | 391 | 1.20× | 1.96× |
| RussianРусский | 951 | 243 | 387 | 1.22× | 1.94× |
| FrenchFrançais | 1,082 | 244 | 278 | 1.22× | 1.39× |
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 Vietnamese prompt costs about $1.20, and about $1.96 on cl100k_base. A document of 1,000 Vietnamese characters is roughly 276 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 Vietnamese text
- Use a model with the newer tokenizer. For Vietnamese, o200k_base used 39% 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 Vietnamese; 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 Vietnamese text?
In our measurement, the same website copy that is 200 tokens in English (902 characters) is 239 tokens in Vietnamese on o200k_base (867 characters) and 391 tokens on cl100k_base. As a rule of thumb, 1,000 characters of Vietnamese come to about 276 tokens on o200k_base and about 451 on cl100k_base.
Is Vietnamese 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 Vietnamese copy needs 1.20× the English token count, so at the same price per token the same message costs about 1.20× as much. It was 1.96× on the older cl100k_base.
Which tokenizer is better for Vietnamese?
o200k_base needed 39% fewer tokens than cl100k_base for Vietnamese in this sample. Across all 29 non-English languages in the data, the average saving was 34%, so Vietnamese improved more than average.
Why does Vietnamese need about as many tokens than English?
Vietnamese uses the Latin alphabet with many accents and tone marks, and some letters carry two marks at once. Each word is short and separated by spaces, but accented letters are multi-byte and the accented forms are rarer in training text, so words can split at the accent. Vietnamese is written in Latin letters, so its basic characters are familiar to every tokenizer, but the many accents and tone marks create characters that are multi-byte and less common. That is why its cost is higher than the other Latin-script languages even though the alphabet looks similar.
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 · Bengali · Thai · Russian · Greek · Turkish · Indonesian · German · Spanish · Portuguese · French