arXiv:2606.18774v1 Announce Type: new Abstract: We present RouteJudge, an online pairwise preference evaluation framework for LLM routing systems, with a public platform available at https://routejudge.cn. Different from model-level response evaluation, RouteJudge focuses on router-level decision quality. For each user query, multiple routing strategies independently recommend candidate models under the same model pool and budget constraints. The selected model responses are then presented to users through anonymous pairwise comparisons, and the resulting user preferences are attributed back t
The proliferation of various LLMs and the need for efficient resource allocation necessitate robust routing and evaluation systems.
Evaluating and optimizing LLM routing directly impacts cost-efficiency, performance, and the user experience of AI applications.
The introduction of open platforms for comparative LLM router evaluation enables more transparent and data-driven decision-making in large-scale AI deployments.
- · AI application developers
- · LLM operators
- · Cloud providers
- · Inefficient LLM routing strategies
- · Proprietary, opaque evaluation systems
Improved resource utilization and performance for large-scale AI systems.
Increased competition and innovation in LLM routing and orchestration solutions.
Potential for new standards and benchmarks in multi-LLM system design and efficiency.
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