How is model cascading different from model routing?

A model router picks which model to use before generating a response, based on input complexity. Cascading actually generates a response with the cheap model first, then evaluates it—only calling the expensive model if the first attempt is insufficient. Cascading catches cases the router would have misclassified.

Model Cascading

Written by Max Zeshut

Founder at Agentmelt · Last updated Sep 9, 2026

An architecture where a fast, cheap model handles the first pass on every request, and only routes complex or low-confidence cases to a larger, more expensive model. Unlike a simple model router that picks one model upfront, cascading tries the small model first, evaluates the output quality, and escalates if needed. This pattern typically reduces inference costs by 50–70% while maintaining the quality ceiling of the most capable model.

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How is model cascading different from model routing?: A model router picks which model to use before generating a response, based on input complexity. Cascading actually generates a response with the cheap model first, then evaluates it—only calling the expensive model if the first attempt is insufficient. Cascading catches cases the router would have misclassified.

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