AutoMix

Confidence-based routing: A small model answers first; a confidence signal determines whether to accept its answer or escalate to a larger model.

Cascading thresholds: Uses multiple confidence thresholds to route queries through a cascade of increasingly capable (and expensive) models.

Cost-quality Pareto: Achieves Pareto improvements over single-model baselines, delivering equivalent quality at substantially lower inference cost.

Production relevance: The pattern maps cleanly onto practical LLM deployment where most queries can be handled by cheap models but a tail of hard queries need the frontier model.