There is a case study comparing with RouteLLM in the appendix.

RouteLLM is essentially a benchmark-driven approach. Their framework chooses between a weak and a strong model and helps developers optimize for a metric called APGR (Average Performance Gap Recovered) — a measure of how much of the stronger model’s performance can be recovered when routing some queries to the weaker, cheaper model. However, their routing models are trained to maximize performance on public benchmarks like MMLU, BBH, or MT-Bench. These benchmarks may not capture subjective, domain-specific quality signals that surface in practice.

Arch-Router takes a different approach. Instead of focusing benchmark scores, we lets developers define routing policies in plain language based on their preferences — like “contract analysis → GPT-4o” or “lightweight brainstorming → Gemini Flash.” Our 1.5B model learns to map prompts (along with conversational context) to these policies, enabling routing decisions that align with real-world expectations, not abstract leaderboards. Also our approach doesn't require router model retraining when new LLMs are swapped in or when preferences change.

Hope this helps.