LLM Model Routing Explained: How Teams Cut AI Costs by Up to 85% in 2026

As the number of viable large language models has grown from a handful to dozens, relying on a single LLM for every task has become both inefficient and costly. Model routing is a middleware layer that sits between an application and a pool of LLMs, directing each request to the most suitable model based on factors like task complexity, cost, latency, and safety requirements. Organizations that have adopted routing report cost reductions of 40–85% with no measurable drop in output quality, according to data from platforms including Requesty, OpenRouter, and InferenceHub. Three main architectural patterns have emerged: simple fallback chains, task-based classification routing, and hybrid approaches combining both. The shift is being driven partly by rising frontier model costs, with companies like Cognition — maker of the Devin AI agent — publicly acknowledging that single-model strategies are becoming financially unsustainable for engineering teams.