The performance of large language models improves as the number of instantiated agents increases. Experimental results show that multiple small models can outperform a larger model. The paper discusses the relationship between performance improvement and problem difficulty, and the author proposes two optimization strategies: progressive sampling with voting and stratified sampling with voting.