MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings
Yiqun Zhang, Hao Li, Zihan Wang, Shi Feng, Xiaocui Yang, Daling Wang, Bo Zhang, Lei Bai, Shuyue Hu
Abstract
Multi-turn, long-horizon tasks are increasingly common for large language models (LLMs), but solving them typically requires many sequential model invocations, accumulating substantial inference costs. Here, we study cost-aware multi-turn LLM routing: selecting which model to invoke at each turn from a model pool, given a fixed cost budget. We propose MTRouter, which encodes the interaction history and candidate models into joint history–model embeddings, and learns an outcome estimator from logged trajectories to predict turn-level model utility. Experiments show that MTRouter improves the performance–cost trade-off: on ScienceWorld, it surpasses GPT-5 while reducing total cost by 58.7%; on Humanity’s Last Exam (HLE), it achieves competitive accuracy while reducing total cost by 43.4% relative to GPT-5, and these gains even carry over to held-out tasks. Further analyses reveal several mechanisms underlying its effectiveness: relative to prior multi-turn routers, MTRouter makes fewer model switches, is more tolerant to transient errors, and exhibits emergent specialization across models.Code: https://github.com/ZhangYiqun018/MTRouter- Anthology ID:
- 2026.acl-long.2045
- Volume:
- Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2026
- Address:
- San Diego, California, United States
- Editors:
- Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 44206–44226
- Language:
- URL:
- https://preview.aclanthology.org/ingest-acl/2026.acl-long.2045/
- DOI:
- Cite (ACL):
- Yiqun Zhang, Hao Li, Zihan Wang, Shi Feng, Xiaocui Yang, Daling Wang, Bo Zhang, Lei Bai, and Shuyue Hu. 2026. MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 44206–44226, San Diego, California, United States. Association for Computational Linguistics.
- Cite (Informal):
- MTRouter: Cost-Aware Multi-Turn LLM Routing with History–Model Joint Embeddings (Zhang et al., ACL 2026)
- PDF:
- https://preview.aclanthology.org/ingest-acl/2026.acl-long.2045.pdf