Model routing allocates queries to the suitable model, improving system performance while reducing costs. However, existing routing methods face practical limitations that hinder scalability in large-scale applications and struggle to keep up with the rapid growth of the large language model (LLM) ecosystem. To tackle these challenges, we propose TagRouter, a training-free model routing method designed to optimize the synergy among multiple LLMs for open-domain text generation tasks. Experimental results demonstrate that TagRouter outperforms 13 baseline methods, increasing the accept rate of system by 6.15% and reducing costs by 17.20%, achieving optimal cost-efficiency. Our findings provides the LLM community with an efficient and scalable solution for model ensembling, offering users an evolvable "super model."
@article{arxiv.2506.12473,
title = {TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks},
author = {Zhou Chen and Zhiqiang Wei and Yuqi Bai and Xue Xiong and Jianmin Wu},
journal= {arXiv preprint arXiv:2506.12473},
year = {2025}
}