Model selection is the decision of which model — or combination of models — to use for a given task. The relevant factors include accuracy requirements, acceptable latency, cost per token, context window size, and domain capability.
Overusing large, expensive models drives up cost and latency unnecessarily. Underusing model capability produces poor results. Matching model to task is one of the most impactful decisions in enterprise AI system design — both for performance and for the economics of running AI at scale.
