Human emotion expressed through body motions is often interpreted differently by different perceivers. We explored the use of large language models (LLMs) to simulate humans’ judgment variability and model the distribution of perceived emotions from body motion. Given textual motion descriptions derived from a multiactor dataset covering diverse emotional contexts, we prompted LLMs to generate emotion probability distributions. We compared LLM-generated outputs with human perception distributions and performer-intended emotion labels. The model showed strong perceptual alignment with human perceivers and achieved an accuracy of 63.2% in identifying the intended emotions, comparable to that of human perceivers. We further investigated the effects of context and scoring constraints, showing that both factors influence the scoring of the model. Our findings suggest that LLMs can serve as effective proxies for modeling emotion perception distributions from motions, supporting scalable evaluation and generation of expressive behaviors grounded in human perception. © 2025 Copyright held by the owner/author(s)