Large language models (LLMs) have become a promising tool for automating complex tasks such as process model generation from text. In order to evaluate the capabilities of LLMs in generating process models, it is crucial to provide means to assess the output quality. A few studies have already provided key performance indicators for assessing aspects such as completeness of the models in a quantitative way. In this paper, we focus on the qualitative assessment of generated process models generated by LLMs based on a user survey. By analyzing user preferences, we aim to determine whether LLM-generated process models meet the needs and expectations of experts. Our analysis reveals that 60% of users, regardless of their modeling experience, prefer LLM-generated models over human-created ground truth models.