The Routing Plateau: Understanding the Accuracy Limits of LLM Routers
LLM routing has become a popular approach to improve the cost-quality trade-off of LLM services by adaptively selecting a model for each query. Recent work has explored a broad range of routing methods, including clustering-based routers, learned classifiers, pairwise ranking, and confidence-based approaches. Our extensive study of 21 routing methods across five benchmarks reveals a consistent phenomenon that we call the routing plateau (Fig. 1): many methods, including kNN, achieve very similar accuracy and converge to a narrow performance range that remains far below the oracle router. Our analysis supports a correctness-prediction bottleneck hypothesis: current routers primarily learn ...
| Authors | Yifan Lu, Qiyue Zhang, Shenrun Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 27 May 2026 |
| Last updated at source | 28 September 2026 |
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