LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers
This work provides a unified infrastructure and benchmark for researchers and developers to compare, develop, and deploy LLM routers, which is crucial for cost-effective LLM deployment.
This paper addresses the challenge of selecting the optimal large language model (LLM) for a given query and budget by proposing a unified framework for LLM routing. Their empirical study shows that learned routers outperform the strongest fixed-model baseline by 14.6% relatively, and lightweight routers are competitive under tight cost constraints.
No single large language model (LLM) is optimal across all queries and budget constraints, making model routing essential for cost-effective deployment. Existing routers adopt diverse formulations and implementations, making fair comparison and extension difficult. We present a unified formulation of LLM routing as a sequential decision process characterized by five components: context encoders, model encoders, scoring functions, decision rules, and learning signals, covering single-turn, multi-turn, and personalized routing. Based on this formulation, we develop an automated pipeline for constructing routing supervision and evaluating routers jointly on response quality and inference cost. The resulting benchmark, xRouteBench, spans generic LLM, memory-augmented, vision, time-series, and personalized routing tasks. We further introduce LLMRouter, an open-source modular infrastructure with more than 16 representative routers. Our empirical study shows that learned routers outperform the strongest fixed-model baseline by 14.6% relatively, lightweight routers become more competitive under tight cost constraints, and user-conditioned routing consistently improves personalization.