We investigate how Vision-Language Models (VLMs) leverage visual features when making analogical comparisons about people. Using synthetic images of individuals varying in skin tone and nationality, we prompt GPT and Gemini models to make analogical associations with desserts and drinks. Results reveal that VLMs systematically associate darker-skinned individuals with brown-colored food items, with GPT showing stronger associations than Gemini. These patterns are amplified in Thai versus English prompts, suggesting language-dependent encoding of visual stereotypes. The associations persist across manipulation checks including position swapping and clothing changes, though presenting individuals alone yields divergent language-specific patterns. This work reveals concerning associations in VLMs’ visual reasoning that vary by language, with important implications for multilingual deployment.