Testing 67 Models: Combining LLMs Rarely Beats the Best Single Model
A study testing 67 large language models found that combining multiple LLMs rarely outperforms the best single model. The research challenges the assumption that model orchestration or ensembling reliably improves performance over using a single top-performing model.
Background
Hugging Face is a platform where AI researchers and developers share models, datasets, and interactive demos (Spaces). This Space tests a common belief in the AI world: that using multiple LLMs together ("orchestration") can outperform a single strong model. The author ran systematic tests on 67 different models and found that combining LLMs — whether through ensembling, routing, or chaining — rarely beats simply using the best single model available for the task. This challenges the growing hype around multi-agent systems and complex LLM pipelines. The takeaway is practical: many engineering efforts to combine models may be wasted, and a simpler "just use the best model" strategy often works better, especially as models like GPT-4 and Claude continue to improve.