Model Routing Is Simple. Until It Isn’t.

Model routing in agentic systems, initially perceived as straightforward, is complex due to the challenges in optimizing costs, task complexity, and latency. While traditional routing treats model selection as a classification problem, this approach fails to account for the intricate dynamics between model pricing, caching effects, and infrastructure conditions. Costs are not just about model token pricing but also involve caching efficiencies that can alter the expected financial outcomes, as illustrated by the unexpected cost-effectiveness of Sonnet over GPT-4.1 in specific scenarios. Task complexity is often underestimated at the routing stage, leading to inefficient model allocation, as unseen intricacies arise during execution. Additionally, latency is influenced by factors beyond model size, such as routing overhead and infrastructure state, which can negate the benefits of theoretically faster models. The approach to routing should shift from a focus on selecting the "best" model to optimizing the entire system's performance across multiple parameters, accommodating compliance, governance, and operational constraints for a more efficient and adaptable system.

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