Is ChatGPT Better in English Than Other Languages?

ChatGPT can produce different quality results depending on the language you use. For many everyday tasks in widely spoken languages, the difference is small. For specialized, technical or nuanced tasks, the choice of language can affect accuracy, tone and detail. The right answer depends on your specific task and the language you are working in.

This is not a question you can answer once for all tasks. A researcher writing in Spanish may get better results than someone prompting in a minority European language, but worse results than the same researcher prompting in English for a technical query. The only reliable way to know is to test. When you find a prompt that works well, a tool like fenbs lets you keep it alongside the project context, so it works consistently across sessions.

Why training data affects language quality

Large language models like ChatGPT are trained on text from across the internet and published sources. More text exists in English than in any other language, which means the model has seen more examples of English writing, more English reasoning, and more English domain knowledge.

This does not mean the model is bad in other languages. For conversational use, translation, creative writing and many professional tasks, it performs well in Spanish, French, German, Japanese, Chinese and other high-resource languages. The gap is more noticeable for tasks that require deep domain knowledge, technical vocabulary, or specific cultural references that are better documented in English.

OpenAI continues to work on multilingual performance, so the gap changes across model versions. Test on the current model rather than relying on reports about an older one.

How to test your specific use case

Before deciding on a language strategy, run the same task twice: once in your native language and once in English. Keep the meaning of the prompt identical and compare the outputs on the dimensions that matter to you.

• Accuracy: did the model get the facts right?
• Completeness: did it address all parts of the question?
• Tone and register: does it match the style you need?
• Domain vocabulary: did it use the right technical terms?
• Fluency: does the output read naturally in your target language?

Run this comparison on a few real examples from your work, not test sentences. A synthetic example may not reveal the gaps that matter for your actual tasks.

When prompting in English may help

For technical domains with strong English documentation — programming, legal research under US or UK law, scientific literature, or medical terminology — prompting in English often produces more detailed and accurate output. This is partly because the training data for these domains skews toward English.

If your final output needs to be in another language, you can prompt in English and ask the model to respond in your language. Say "respond in Portuguese" at the end of the prompt. This approach gives the model the benefits of English training data while producing output in your language, though you should check whether the translation step introduces errors.

When writing in your native language makes sense

For content that depends on cultural specifics — local idioms, region-specific professional norms, culturally appropriate tone — prompting in your language is often better. The model understands context it cannot translate cleanly from English.

For practical day-to-day writing tasks in a language the model handles well — drafting emails, summarizing documents, editing copy — using your native language is simpler and usually produces natural, fluent output. The cost of translating to English and back rarely pays for itself.

Mixed approaches

Some teams use English for the structural parts of a prompt — role, format instructions, what to include and exclude — and their native language for the content. For example, an instruction like "Summarize the following in three bullet points, in Spanish:" prompts in English but asks for a Spanish output.

You can also include a glossary of specialized terms in your prompt. If your field uses specific vocabulary that the model might translate inconsistently, include a short list of terms and their correct equivalents in your language. This is especially useful for legal, medical and technical work.

Record which language works for which task

Once you have tested a task in both languages and found which produces better results, save that finding. Write the prompt in the language that worked, with a note on what you tested and what the difference was. The next time you tackle the same task, you start from a confirmed approach rather than repeating the test.

How to do this with fenbs

1. Create a free account and add a project for the work you are doing in ChatGPT.
2. Add a Skill for each high-value prompt you use, noting the language, the task type, and what you tested it against.
3. Record any standing terminology rules as Decisions and rules: terms to use, terms to avoid, language-specific conventions.
4. When a connected AI assistant reads your project context, it sees those rules before every session, whether you are working in English or another language.
5. Update the Skill each time you refine a prompt so your team always uses the current version.

For more prompt strategies across different AI tools, browse the AI prompts topic hub.