What is an AI Model Router?

Not every request needs the largest, most capable, most expensive model available. A router examines a request, or the type of task it represents, and picks a model suited to it: a quick classification or extraction task might route to a small, fast, cheap model, while a task requiring deep multi-step reasoning routes to a more capable, more expensive one. This lets a system control cost and latency without sacrificing quality on the tasks that actually need a stronger model. Routers also provide resilience: if a preferred provider is unavailable or degraded, a well-built router can fail over to an alternate model or provider in a defined fallback chain, keeping the system operational rather than failing outright. Effective routing decisions are usually informed by a mix of static rules (this task category always uses this model) and empirical performance data (this model has measurably higher accuracy on this task type in practice), rather than a single static assignment.