Large language models (LLMs) could widen access to mental health support, but support should also suit the person’s culture. This work asks what LLMs offer in the silver-linings exercise of Positive Affect Treatment (PAT), where a person lists positive aspects of an event. Following appraisal theory, we treat each silver lining as a claim that the event serves a goal. We wrote 18 high-school events in four versions, set in the United States or Thailand and in English or Thai. Two widely used LLMs gave six silver linings for each version, and two LLM coders coded which goal each silver lining serves and who gains from it, across all 864 lists of silver linings. Growth, what the student learned or can now do, was the most frequent goal in every condition and for both models. Without the country named, the share of linings that benefit others or the group changed by under three percentage points with language, setting, or their interaction. Naming the country raised the setting contrast by under five points. These results suggest little sensitivity to cultural cues, so a system that uses these models to guide the exercise cannot assume its suggestions fit the person asking.