When a Smaller Model Is the Better Catalog Worker

For high-volume product tagging, a frontier model’s breadth can be overkill. AWS argues that when a catalog’s taxonomy is stable and outputs can be scored programmatically, a smaller open-weight model can fit better: teach it the required schema, then tune the balance between missing tags and unnecessary ones. That is a workflow-design choice aimed at consistency—not proof that every smaller model will outperform a larger one.