Introducing RouterBench

Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, with new models being introduced at an unprecedented pace. However, no single model can achieve optimal performance for all applications while remaining cost-effective. Just as in the early days of cloud computing, AI developers face a tradeoff between capability and affordability. High-end models like GPT-4 may be the Lamborghinis of the AI world, but their high cost and latency makes them unsuitable to many applications. Techniques like prompt engineering, quantization, and systems optimization offer ways to increase performance or reduce cost on a cheaper model. But as the LLM landscape grows more crowded by the day, striking an optimal balance between capability and cost just keeps getting more complex. Enter the router. In the simplest terms, LLM routing involves dynamically selecting the optimal model for each prompt based on the nature of the input. Instead of committing to a sin

Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, with new models being introduced at an unprecedented pace. However, no single model can achieve optimal performance for all applications while remaining cost-effective. Just as in the early days of cloud computing, AI developers face a tradeoff between capability and affordability. High-end models like GPT-4 may be the Lamborghinis of the AI world, but their high cost and latency makes them unsuitable to many applications. Techniques like prompt engineering, quantization, and systems optimizati