Although large language models have demonstrated strong performance on algorithmic and mathematical tasks, their suitability as social science research assistants remains unclear, particularly because many social science questions lack agreed-upon, quantifiable benchmark answers. Evaluating this suitability is important since researchers and students increasingly rely on AI to generate research ideas. When questions concern the countries in which models were developed, bias may be exacerbated: Do AI models favour the countries where they were developed or the social theories associated with those countries' scholarly traditions? Such biases could distort research findings, undermine scientific credibility, and distort findings when they are disseminated to the public. To investigate whether models exhibit country-focused bias in social science research, we designed a benchmarking task requiring AI to write short essays on social science questions in relation to the US and China as well as some broader social science questions. We varied three conditions: question type (cross-country comparisons, country-specific questions, and general questions); model (two US-developed, two China-developed, and one Europe-developed model); and prompting modality (a base essay-writing request, a researcher identity, a US researcher identity, and a Chinese researcher identity). Responses were manually annotated for bias and source use by two researchers with experience in social science in relation to China and the US. Our analyses show that, on the whole, US and Chinese models and the European one are not politically biased but differ in the sources they cite. Still, some comparative questions favour one country over another, and this tendency is more pronounced for certain questions. These findings suggest that researchers and students should exercise caution when using AI for social science research.

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