Model Router picks a model by price, latency and task complexity

Not every task needs the most powerful model. A simple classification request or a short rephrasing can be handled by a fast, cheap option, while complex reasoning or analysis of a long document needs a stronger model. Send everything to one, and you either overpay or lose quality.

Model Router in Korveniq AI picks a model for each request by three parameters: price, latency and task complexity. Real-time routing cuts inference cost while keeping quality on hard scenarios.

One model for every task is costly or imprecise

Choosing a single model forces a compromise. A powerful model delivers quality but makes even trivial requests expensive and slow. A light one saves money but errs where deep reasoning is needed.

Real workloads are uneven: most requests are simple, fewer are hard. The router exploits that unevenness.

How the routing decision is made

For each request, complexity is assessed: task type, context size, need for multi-step reasoning. Based on that assessment and the current limits on price and acceptable latency, a suitable model is chosen.

Routing rules are tuned to the scenario: for latency-sensitive dialogues speed takes priority, for analytics quality does.

Quality on hard scenarios is preserved

Savings must not come at the expense of quality. Complex requests still go to stronger models, and results are checked against evaluation sets for each route.

If a lighter model is unsure of its answer, the request can be escalated to a stronger one — so the boundary between routes stays flexible.

Simple requests go to fast models, complex ones to stronger models.

Flexibility when models change

Because scenarios call the router rather than a specific model, a model can be replaced or added centrally, without rewriting integrations or changing the scenarios themselves.

The Korveniq AI team can help tune routing rules to your workload and measure the effect on real requests.