TL;DR: No single LLM is optimal for every query and budget. This work unifies the scattered field of LLM routing into "five building blocks" and ships an open-source infrastructure bundling 16+ routers with a dedicated benchmark. Title: LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers URL: Key points 🧩 Decomposes any router into 5 components: Context Encoder / Model Encoder / Scoring / Decision Rule / Learning Signal 📊 xRouteBench spans 5 tracks (Generic, Memory, Vision, TimeSeries, Personalized), 4,767 queries total 🛠️ A dense query-model matrix, built by dispatching every query to all 18 candidate models, serves as both training supervision and test bed 🚀 Learned routers beat the strongest fixed-model baseline (always pick the largest) by 14.6% relative ⚖️ No router dominates: RouterDC leads on accuracy but drops to 10th under tight cost constraints 🔁 Multi-turn routing adds cost without consistent gains (Router-R1 at just 22.3%) 👤 User conditioning helps, but the best design flips between simulated personas and real feedback A solid step that proves "the best router depends on task and budget" and lays a foundation for fair comparison. #LLM# #ModelRouting#