How does the router decide which model to use?

Common approaches: (1) keyword/pattern matching for simple routing, (2) a small classifier model that evaluates request complexity (fast and cheap), (3) trying a small model first and escalating if confidence is low (cascading), (4) heuristics based on request length, topic, or user tier. Most production systems use a lightweight classifier trained on labeled examples of easy/medium/hard requests.

Model Router

Written by Max Zeshut

Founder at Agentmelt · Last updated May 26, 2026

A system that dynamically selects which AI model to use for each request based on task complexity, latency requirements, and cost constraints. Instead of routing all requests to the most expensive frontier model, a model router sends simple tasks (FAQ answers, classification) to fast, cheap models and complex tasks (multi-step reasoning, code generation) to capable but expensive models. Model routing can reduce AI agent costs by 50–70% while maintaining quality.

Example

A support agent processes 10,000 tickets per month. The model router classifies each ticket: password resets and account questions (70% of volume) route to Haiku ($0.001/ticket), product troubleshooting (25%) routes to Sonnet ($0.008/ticket), and complex escalations (5%) route to Opus ($0.03/ticket). Monthly LLM cost drops from $300 (all-Opus) to $25.

Frequently asked questions

How does the router decide which model to use?: Common approaches: (1) keyword/pattern matching for simple routing, (2) a small classifier model that evaluates request complexity (fast and cheap), (3) trying a small model first and escalating if confidence is low (cascading), (4) heuristics based on request length, topic, or user tier. Most production systems use a lightweight classifier trained on labeled examples of easy/medium/hard requests.

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