AI Model Routing: Send Each Task to the Right Engine

Model routing means classifying each request before you spend a single token. A quick caption or tag can go to a fast, cheap model, while multi-step reasoning, code, or ambiguous prompts get escalated to a frontier model. The gate is simple: match task difficulty to the smallest model that still clears your quality bar.

Build the router on signals you already have — prompt length, whether tools or JSON output are required, image versus video versus text, and any user-tier flag. On CinderHub we route chat, image, video, and storyboard jobs through one dispatcher so each modality hits a model tuned for it, instead of forcing everything through one expensive default.

Then measure and adjust. Log per-route cost, latency, and a cheap quality check (retries, thumbs-down, human overrides), and move the threshold when a smaller model starts failing. Add a fallback: if the cheap model returns low confidence or malformed output, retry once on the stronger tier before giving up.