Question 1

Why does non-English text cost more tokens?

Accepted Answer

Because the tokenizer was trained mostly on English. Large language models do not read characters, they read tokens — chunks the tokenizer carved out of its training data — and it learned long, efficient chunks for the language it saw most, which is English. A common English word is often a single token; the same word in another language may be split into several pieces, and text in a non-Latin script like Arabic, Hindi or Japanese is frequently broken down almost character by character, with some characters costing more than one token because they are encoded as multiple bytes. The result is that the identical meaning, expressed in another language, uses more tokens — and since APIs bill per token, it costs more. Modern tokenizers such as o200k are better at this than older ones, but the gap has not closed: serving the same product in Hindi or Thai can still cost two to three times what it costs in English. This calculator turns that ratio into a dollar figure for your own volume.

Question 2

How much more does each language cost?

Accepted Answer

It varies by script and by tokenizer, but the pattern is consistent. Western European languages that share the Latin alphabet — Spanish, French, German, Portuguese, Italian — carry a modest premium, usually around 1.2 to 1.3 times English, because accented characters and longer words add tokens without changing the script. Languages in other scripts cost much more: Russian and other Cyrillic languages land near 1.9 times, Chinese around 1.4 to 1.5, Japanese and Korean between 1.6 and 1.8, and the heaviest — Arabic, Hindi, Bengali, Thai — run from 2 to nearly 3 times English for the same content. These multipliers are approximate and depend on the exact tokenizer, so this calculator lets you override the figure with a measured value for your own text. The point is not the second decimal place; it is that a global user base does not cost a flat per-user rate, and the languages you might most want to reach are often the most expensive to serve.

Question 3

Does the overhead apply to output as well as input?

Accepted Answer

Yes, and that matters because output tokens are usually priced several times higher than input tokens. When the model replies in the same language the user wrote in, its reply is tokenized with the same inefficiency, so a non-English answer uses proportionally more output tokens too. Since output is the expensive side of most pricing, the language premium falls hardest exactly where it hurts most. This calculator applies the multiplier to both the input and the output token counts and prices them at their separate rates, so the extra cost it reports reflects the full round trip, not just the prompt. If your product generates long answers — summaries, explanations, generated documents — in a user's own language, the output overhead can dominate the total, and trimming prompts or caching a shared system preamble will not touch it, because the cost is in the language of the reply itself.

Question 4

How can I reduce the multilingual token premium?

Accepted Answer

There are a few levers, and this calculator helps you see which are worth pulling. The first is model choice: tokenizers differ, so a model with a more multilingual tokenizer can cut the multiplier for a given language, and comparing the effective cost per request across models can outweigh a difference in the headline per-token price. The second is scope: if a language shows a heavy multiplier and low volume, it may be cheaper to translate to and from English around a cheaper English-only pipeline than to run the model natively in that language — though that adds a translation step and its own cost. The third is simply pricing and planning: knowing that your Hindi or Japanese users cost two to three times your English ones lets you set regional pricing, forecast budgets honestly, and avoid the nasty surprise of a launch in a new market quietly doubling a slice of your bill. The calculator's per-language table is built for exactly that comparison — it shows the multiplier, the monthly cost and the premium side by side so the expensive languages are obvious at a glance.

Calculadora de custos indiretos de token multilíngue

Custo por idioma no seu volume

Uma base global de usuários não é uma taxa fixa

Como funciona esta calculadora

Perguntas frequentes

Por que o texto que não está em inglês custa mais tokens?

Quanto custa cada idioma a mais?

A sobrecarga se aplica tanto à saída quanto à entrada?

Como posso reduzir o prêmio do token multilíngue?