Measuring political bias in large language models (LLMs) remains challenging as it can manifest through subtle differences in framing, argumentation, and legal reasoning that are difficult to capture with a single metric. In this work, we introduce POLI-BIAS, a counterfactual framework for measuring whether LLMs treat legally equivalent conflict scenarios differently depending on the countries involved. POLI-BIAS compares responses to paired prompts in which country identities are systematically swapped across diverse geopolitical relationships, legal violations, and reasoning tasks. Rather than reducing bias to a single judgment, our framework decomposes response disparities into five interpretable dimensions, revealing how and where unequal treatment manifests. Across 5,600 prompt pairs and seven open-weight LLMs from diverse model families and sizes, we find that models treat countries in active conflict or tension less evenly than fictional or uncontested countries, describing, evaluating, and defending equivalent actions differently. Our results thus establish POLI-BIAS as a fine-grained framework for auditing political even-handedness in LLMs.

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