This research paper explores the concept of ensemble dispersion in language models, specifically focusing on the diversity of perspectives generated by multiple models. The study presents a method to measure diversity and examines how model identity contributes to the diversity of ensemble outputs. The findings suggest that model identity is a significant factor in shaping the diversity of ensemble outputs, but the relationship is complex and influenced by various factors. This research has implications for the development and deployment of AI agents that rely on ensemble models.