What is Model Router?
TL;DR
A system that intelligently routes user queries to the most suitable AI model based on task type, complexity, cost, and latency requirements.
Model Router: Definition & Explanation
A Model Router is an intelligent middleware layer that analyzes incoming queries and directs them to the optimal AI model based on criteria such as task complexity, required capabilities, cost efficiency, and latency constraints. Rather than sending all requests to a single large model, a model router evaluates each query and selects the most appropriate model from a pool — routing simple factual questions to smaller, faster, and cheaper models while directing complex reasoning tasks to larger, more capable ones. This approach can reduce API costs by 50-80% while maintaining output quality. Key routing strategies include classification-based routing (using a lightweight classifier to categorize queries), cascade routing (trying a small model first and escalating if the response quality is insufficient), and embedding-based routing (matching query embeddings to model capability profiles). Major implementations include OpenRouter, Martian's model router, and custom routing layers built with frameworks like LiteLLM. As the number of available AI models continues to grow, model routers are becoming essential infrastructure for organizations managing multi-model AI deployments.