Model Cascading
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.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Frequently asked questions
- 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.