Model routing is the dynamic selection of which language model handles each request based on task complexity, cost constraints, latency requirements, or content classification.
Model routing is the dynamic selection of which language model handles each request based on task complexity, cost constraints, latency requirements, or content classification. Instead of sending all requests to a single model, a router analyzes each request and dispatches it to the most appropriate model in a fleet.
The simplest routing strategy uses task classification: simple factual questions go to a small fast model, complex reasoning tasks go to a large capable model, and code generation goes to a code-specialized model. More sophisticated approaches use a lightweight classifier trained on historical data to predict which model will produce the best response for each input, optimizing the cost-quality Pareto frontier.
Routing architectures include cascading (try small model first, escalate to larger model if confidence is low), parallel (query multiple models and select best response), and semantic (route based on content domain). The router itself can be a small model, a classifier, or rule-based logic. The key metric is routing accuracy: how often the router's selection matches what a quality evaluator would have chosen after seeing all models' outputs.
Model routing reduces inference costs by 50-80% while maintaining quality. Most production workloads contain a mix of easy and hard requests — routing easy requests (often 60-70% of traffic) to cheaper models saves significant compute without degrading the user experience for those interactions.
A conversational AI platform routes requests through a lightweight classifier: greetings and simple questions go to Haiku (fast, cheap), multi-step reasoning and analysis go to Sonnet (balanced), and critical business decisions requiring maximum accuracy go to Opus (highest quality). This reduces average cost per request by 65% compared to sending everything to the most capable model.
Aaron is an engineering leader, software architect, and founder with 18 years building distributed systems and cloud infrastructure. Now focused on LLM-powered platforms, agent orchestration, and production AI. He shares hands-on technical guides and framework comparisons at fp8.co.