Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey

TL;DR

This analysis demonstrates that effective multi-LLM routing requires balancing competing objectives, and introduces a conceptual framework that characterizes routing systems along three dimensions: when decisions are made, what information is used, and how they are computed.

Abstract

The rapid growth of large language models (LLMs) with diverse capabilities, costs, and domains has created a critical need for intelligent model selection at inference time. While smaller models suffice for routine queries, complex tasks demand more capable models. However, static model deployment does not account for the complexity and domain of incoming queries, leading to suboptimal performance and increased costs. Dynamic routing systems that adaptively select models based on query characteristics have emerged as a solution to this challenge. We provide a systematic analysis of state-of-the-art multi-LLM routing and cascading approaches. In contrast to mixture-of-experts architectures, which route within a single model, we study routing across multiple independently trained LLMs. We cover diverse routing paradigms, including query difficulty, human preferences, clustering, uncertainty quantification, reinforcement learning, multimodality, and cascading. For each paradigm, we analyze representative methods and examine key trade-offs. Beyond taxonomy, we introduce a conceptual framework that characterizes routing systems along three dimensions: when decisions are made, what information is used, and how they are computed. This perspective highlights that practical systems are often compositional, integrating multiple paradigms under operational constraints. Our analysis demonstrates that effective multi-LLM routing requires balancing competing objectives. Choosing the optimal routing strategy depends on deployment and computational constraints. Well-designed routing systems can outperform even the most powerful individual models by strategically leveraging specialized capabilities across models while maximizing efficiency gains. Meanwhile, open challenges remain in developing routing mechanisms that generalize across diverse architectures, modalities, and applications.

Figures (3)

• Figure 1: Arch-Router's Preference-Aligned Routing Mechanism. The routing policies and user conversation are provided to the router to select the appropriate policy and corresponding LLM. Example usage in coding is shown on the right.
• Figure 2: UniRoute cluster-based router. First, perform $K$-means on a training set to find K centroids, and then partition the validation set into K representative clusters. Each test-time LLM can then be represented as a $K$-dimensional feature vector of per-cluster errors. This yields an intuitive routing rule: for each test prompt, route to the LLM with the smallest cost-adjusted average error on the cluster the prompt belongs to. The prompt embedder may either be completely unsupervised (as shown in the figure), or fitted via supervised learning using labels from a set of training LLMs different from those seen during test time.
• Figure 3: AutoMix example for two-model setup. Instead of relying only on small model (SLM) with low performance or a large model (LLM) with high cost, AutoMix automatically mixes multiple black-box language models, based on user desired cost-quality tradeoff. AutoMix works in a 3-step process: (i) generation by a small model (LM1), (ii) self-verification of the generated answer, (iii) using confidence assessments from self-verification to do appropriate routing to a larger model (LM2). For N-model setup, the process is repeated till the final answer is reported.