Model routing is probably more useful when treated as a workload optimization problem rather than simply a way to pick the cheapest model. A 10x reduction in token price can disappear quickly if the cheaper model needs more retries, longer prompts, or additional validation to reach the same task-level outcome.

In production AI work at IT Path Solutions, I’d measure routing decisions against cost per successful outcome, latency, failure rate, and escalation frequency not just cost per million tokens. That also makes the open-weight vs. frontier-model comparison more meaningful: the right question is which model delivers the required quality and reliability at the lowest total cost for that workload.

The interesting next step is dynamic routing based on observed task performance, where the router learns which model is actually economical for each class of work rather than relying only on predefined model tiers.