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Written by Max Zeshut
Founder at Agentmelt
A component that dynamically picks which LLM (fast/cheap vs. large/capable) to use for each individual request based on complexity, cost budget, or required accuracy. Model routers enable agents to serve the long tail of simple queries with cheap models while reserving expensive reasoning models for hard cases—often cutting total inference spend by 40–70% with no drop in user-facing quality.