Recent advancements in Large Language Models (LLMs) have paved the way for enhanced natural language understanding and generation capabilities. The Mixture-of-Agents (MoA) architecture presents a layered approach combining multiple LLM agents to leverage their collective expertise. The MoA models have demonstrated exceptional performance on various benchmarks, surpassing even GPT-4 Omni. This paper signifies a shift towards harnessing the full potential of multiple LLMs for improved AI applications.

• The MoA methodology integrates the strengths of diverse LLMs in a layered architecture.
• State-of-art performance achieved on AlpacaEval 2.0, MT-Bench, and FLASK.
• MoA using only open-source LLMs leads AlpacaEval 2.0 by a significant margin.

This paper underscores the importance of collaborative approaches in enhancing LLM capabilities. It opens doors for further research in multi-agent systems and the optimization of collective intelligence for AI applications.