How does an inference router decide which model to use?

Routers use heuristics like input length, keyword signals, and task type to estimate complexity. More advanced routers use a small classifier model to predict which model will handle each request best. The goal is to match model capability to task difficulty.

Inference Routing

Written by Max Zeshut

Founder at Agentmelt

Directing AI requests to different models or endpoints based on task complexity, cost, or latency requirements. A router might send simple classification tasks to a small, fast model and complex reasoning tasks to a larger, more capable one. This reduces costs by 40–70% compared to routing everything through the most powerful model, while maintaining quality where it matters.

Example

A support agent routes simple FAQ lookups to Haiku (fast, cheap) and complex troubleshooting to Opus (accurate, slower). The router decides based on the estimated complexity of each incoming ticket.

Frequently asked questions

How does an inference router decide which model to use?: Routers use heuristics like input length, keyword signals, and task type to estimate complexity. More advanced routers use a small classifier model to predict which model will handle each request best. The goal is to match model capability to task difficulty.

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