Abstract Vision–language models (VLMs) increasingly evaluate visual content, yet their behaviour across cultural traditions remains poorly characterised. We show that two open-weight VLMs, Qwen3-VL-8B and Llama-3.2-11B-Vision, assign systematically lower scores to East Asian art than to Western art, with Cohen’s d=−0.46 and −0.36 (p < 10−16). Internal probing reveals this disparity is accompanied by higher encoding cost under the primary reference sentence, elevated perplexity, reduced token confidence, and increased hidden-state norms. Across the two tested models, fifteen of eighteen signals shift in the same direction, indicating a consistent cross-model pattern; Llama cross-attention entropy shows a large effect (d=+1.30, p < 10−178). The gap persists across decoding temperatures (T ∈ {0, 0.5, 1}; all p < 10−7), suggesting that it is not driven solely by stochastic decoding. Hidden-state clustering places East Asian art in broadly overlapping representational regions relative to Western art, and keyword-based response analysis shows frequent Chinese lexical attribution for Korean artworks (88% Qwen, 65% Llama). In Qwen, matched-complexity analysis shows 87% of the score gap persists after controlling for image spectral properties. These results show that internal probing can help detect cross-cultural processing differences in the tested VLMs.