Same Model, Different Politics? How Language Shapes AI Ideology

Recent work measures ideological positioning and drift in large language models (LLMs), but typically assumes that those measurements are invariant to the language of evaluation. This paper tests that assumption using the full Political Compass questionnaire in English and Spanish across three generations of OpenAI models, together with a benchmark comparison against recent Qwen and Mistral releases. Using matched item-level responses, we estimate within-model Spanish–English displacement and assess how language choice affects cross-model comparisons. We find that measured ideological coordinates remain in the same broad region across languages, but are not language-invariant. Spanish–English shifts differ in sign and magnitude across models and axes, and in several cases amount to a substantial share of the inter-model dispersion typically interpreted as ideological drift in English-only audits. The implication is methodological: ideological drift should not be treated as a language-invariant property of a model, but as a measurement outcome conditional on language choice and instrument design. Multilingual audits should therefore report language-specific placements and within-model cross-language displacement rather than extrapolating from English-only measurements.