LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing

• URL: http://arxiv.org/abs/2601.07206v1
• Date: Mon, 12 Jan 2026 05:01:15 GMT
• Title: LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing
• Authors: Hao Li, Yiqun Zhang, Zhaoyan Guo, Chenxu Wang, Shengji Tang, Qiaosheng Zhang, Yang Chen, Biqing Qi, Peng Ye, Lei Bai, Zhen Wang, Shuyue Hu,
• Abstract summary: Large language model (LLM) routing assigns each query to the most suitable model from an ensemble.<n>We introduce LLMBench, a large-scale benchmark and unified framework for LLM routing.<n>It comprises over 400K instances from 21 datasets and 33 models.
• Score: 44.046399484829635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM) routing assigns each query to the most suitable model from an ensemble. We introduce LLMRouterBench, a large-scale benchmark and unified framework for LLM routing. It comprises over 400K instances from 21 datasets and 33 models. Moreover, it provides comprehensive metrics for both performance-oriented routing and performance-cost trade-off routing, and integrates 10 representative routing baselines. Using LLMRouterBench, we systematically re-evaluate the field. While confirming strong model complementarity-the central premise of LLM routing-we find that many routing methods exhibit similar performance under unified evaluation, and several recent approaches, including commercial routers, fail to reliably outperform a simple baseline. Meanwhile, a substantial gap remains to the Oracle, driven primarily by persistent model-recall failures. We further show that backbone embedding models have limited impact, that larger ensembles exhibit diminishing returns compared to careful model curation, and that the benchmark also enables latency-aware analysis. All code and data are available at https://github.com/ynulihao/LLMRouterBench.

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