AI Model Routing: Send Each Task to the Right Model

Model routing means classifying each request before you run it, then sending it to the model that fits — not defaulting every prompt to your most expensive one. Draft a quick tweet, extract fields from JSON, or answer an FAQ? A small fast model handles it. Multi-step reasoning, code, or nuanced tone? Escalate to a frontier model. The router itself can be a cheap classifier or a few keyword and token-length rules.

Start with three tiers and measurable gates: a light tier for formatting and classification, a mid tier for standard drafting, and a top tier for reasoning, long context, and anything client-facing. Route on signals you already have — prompt length, task type, whether output ships externally — and always add a confidence fallback so low-certainty calls escalate instead of failing silently. Log every decision so you can audit cost per task type later.

On CinderHub we apply the same logic across chat, image, video, and storyboards: routine variations run on lean models while hero deliverables get the strongest one. The payoff is real — teams routinely cut spend 40–70% with no visible quality drop, because most traffic never needed the flagship. Measure quality per tier, review misroutes weekly, and tighten the thresholds as your traffic mix shifts.