Multilingual Token Overhead Calculator

A global user base is not a flat rate

The cleanest way to blow a launch budget is to price a product in English tokens and then open it to the world. Models bill per token, tokens are what the tokenizer carves out of text, and the tokenizer learned its most efficient chunks from English — so the same sentence, translated, is more tokens, and more tokens is more money. For the Latin-script languages the tax is mild; a Spanish or German user costs perhaps a fifth more than an English one. But the moment you serve a different script the number jumps: Cyrillic nearly doubles, Japanese and Korean sit well above one and a half, and Arabic, Hindi and Thai can run to three times English for identical content — and because output tokens cost several times more than input tokens, the premium bites hardest on the model's reply, the expensive half of the bill. None of the usual savings tricks help here: trimming the prompt or caching a shared preamble cuts English overhead, not the language of the answer. What helps is knowing the number before you commit — which languages you are subsidising, by how much, and whether a cheaper English pipeline wrapped in translation would undercut running the model natively. Price a single request first with the token cost calculator, count tokens for a specific string with the token counter, and if the fix is switching to a model with a friendlier tokenizer, compare the effective rates on the model comparison page.