Multilingual Prompt Converter

Major AI models like GPT-4o, Claude 3.5, and Gemini 1.5 were primarily trained on English text, but they've also been trained on large amounts of multilingual content. The practical result is that these models can respond in most major world languages, but with varying quality levels - English generally produces the best responses, followed by European languages, then Asian languages, then lower-resource languages.

GPT-4o has the strongest multilingual capabilities of current models, having been trained on a particularly large and diverse multilingual dataset. Claude 3.5 has excellent performance in European languages (especially French, German, Spanish, Italian, Portuguese) but is somewhat weaker in Asian languages. Gemini was designed with multilingual capability as a priority and handles Asian languages, particularly Japanese and Chinese, especially well.

Importantly, you can write a prompt in English and ask the model to respond in another language - this often produces better results than writing the entire prompt in the target language, because the model understands English instructions more reliably than instructions in other languages.

The most reliable multilingual prompting technique is to write your instructions in English and add an explicit language instruction at the end: "Respond in [language]" or "Provide your response entirely in [language] with no English text." This gives the model the clarity of English instructions while ensuring the output is in your target language.

For Spanish prompting, be aware that the model will default to a neutral Spanish register unless you specify. Add "Usa el espanol de [Mexico/Spain/Argentina]" to get regionally appropriate vocabulary and spelling. For formal contexts, add "Utiliza un registro formal" (use formal register). For Portuguese, always specify "portugues brasileiro" or "portugues europeu" since the two variants differ significantly.

Japanese prompting requires attention to politeness levels. Japanese has distinct formality registers (casual, polite, very formal/keigo) that change vocabulary, verb endings, and even sentence structure. Specify "丁寧語で書いてください" (please write in polite language) for business contexts or "敬語を使ってください" (please use keigo) for very formal contexts.

Non-English text generally uses more tokens than equivalent English text, which affects API costs. Japanese, Chinese, and Korean text is particularly token-expensive because these languages use characters that don't map efficiently to the BPE (Byte Pair Encoding) tokenization used by most AI models. A Japanese sentence that takes 30 English words to express might use 40-60 tokens instead of the 30 an English version would use.

European languages (French, Spanish, German, Portuguese, Italian) have token costs closer to English, typically 10-30% higher. For languages with extended character sets or diacritics, the overhead is minimal. For completely different scripts (Arabic, Hebrew, Thai, Devanagari), the token overhead can be 2-3x compared to equivalent English text.

If you're building AI applications that need to handle multiple languages at scale, factor language-specific token overhead into your cost models. A multilingual customer support application processing Japanese tickets will have significantly higher token costs per request than the same application processing only English.