Reasoning language models perform well on complex tasks but are costly to deploy due to their size and long reasoning traces. We propose a routing approach that assigns each problem to the smallest model likely to solve it, reducing compute without sacrificing accuracy. Using intermediate representations from s1.1-32B, we train lightweight predictors of problem difficulty or model correctness to guide routing across a pool of reasoning models. On diverse math benchmarks, routing improves efficiency over random assignment and matches s1.1-32B's performance while using significantly less compute. Our results demonstrate that difficulty-aware routing is effective for cost-efficient deployment of reasoning models.
@article{arxiv.2511.03808,
title = {Optimizing Reasoning Efficiency through Prompt Difficulty Prediction},
author = {Bo Zhao and Berkcan Kapusuzoglu and Kartik Balasubramaniam and Sambit Sahu and Supriyo Chakraborty and Genta Indra Winata},
journal= {arXiv preprint arXiv:2511.03808},
year = {2025}
}