AI models like GPT‑4 and Claude are powerful—but are they equally effective in English and French? Let’s explore strengths, differences, and what it means in real multilingual use.

How AI Performs in English vs French

Studies show that GPT‑4 performs similarly in English and French on clinical tasks, with accuracy rates of 35.8% in English and 28.4% in French (difference not statistically significant) on ophthalmology diagnostics involving medical images BioMed CentralPubMed.

However, broader research indicates that LLMs consistently perform better in English, especially for complex reasoning, summarization, and low-resource language tasks aclanthology.orgWIRED.

Why These Differences?

AI models are primarily trained on English-heavy datasets, which leads to stronger language comprehension and reasoning in English. For specialized or technical prompts, English often yields better quality aclanthology.org. Even though GPT‑4 can respond well in French, subtle context, slang, or domain-specific idioms sometimes result in less precise output.

Real-World Examples

In healthcare diagnostics, GPT‑4 fared comparably in both languages—information and image context helped match performance levels PubMed+1BioMed Central+1.

In everyday tasks and knowledge queries, AI tends to default toward English phrasing and structure for clarity and consistency linkedin.com+3WIRED+3BioMed Central+3.

While GPT-4 and similar LLMs can operate effectively in both languages, English still has an edge, especially for reasoning, nuance, and specialized domains. For multilingual users, it’s important to test outputs and, where needed, apply human linguistic expertise to ensure accurate communication across languages.

If AI performs differently in English and French, what happens when we mix the two?
In our next article, we explore how LLMs respond to mixed-language prompts—also known as code-switching—and why this matters for real multilingual communication.

👉 Read next: Can AI Handle Mixed-Language Prompts?