Claude AI Responds Differently Based on Language, Study Finds

A new study from Anthropic reveals that Claude, its flagship AI chatbot, exhibits measurably different behaviors depending on the language in which users interact with it. The research analyzed more than 300,000 anonymized conversations across 20 languages and found systematic variations in tone, directness, and approach.

Language shapes AI personality

Anthropic examined conversations from a two-week period in May 2026 across three Claude models: Sonnet 4.6, Opus 4.6, and Opus 4.7. Using dimensionality reduction techniques, researchers measured responses along four axes: warmth versus rigor, depth versus brevity, candor versus execution, and deference versus caution.

The warmth-versus-rigor axis showed the most significant variation. Arabic responses demonstrated notably greater warmth, incorporating more polite phrasing, humor, playfulness, and affirmation. Dutch responses, meanwhile, scored higher on candor, offering more honest assessments of potential shortcomings.

Anthropic emphasized that these findings reflect behavioral patterns in outputs rather than suggesting the AI holds intrinsic values. The company has not yet identified which specific properties of each language drive these differences.

Model differences compound language effects

The study also uncovered variations between Claude's own models. Sonnet 4.6, the default free version, tends to be more affirming of user ideas and offers comfort without passing judgment. Opus 4.7, the premium paid model, openly critiques ideas and questions assumptions.

These combined differences create scenarios where identical queries could yield substantially different responses. Anthropic cited an example of two users requesting feedback on the same business plan—one in Hindi, the other in Russian—and receiving assessments framed with different values and approaches.

Why it matters

These language-dependent variations have practical implications for global businesses and multilingual teams using AI tools. If Claude provides warmer, more affirming feedback in one language but more critical analysis in another, teams working across languages may receive inconsistent guidance on the same problems. Organizations deploying AI assistants internationally need to account for these behavioral differences when standardizing workflows or comparing outputs.

Study limitations and next steps

Anthropic acknowledged significant limitations in the research. Some languages had far more data than others, and certain languages were overrepresented in professional writing contexts, making the dataset unevenly distributed. These imbalances could skew results.

The company framed the study as an effort to address hidden biases and language-specific gaps in AI training. Anthropic stated it is working to make sense of these variations and using the findings to improve Claude's behavior, though the underlying causes remain an open question.

The findings were first reported by The Week, with additional coverage from The Decoder, The Indian Express, and The Register.