The quick read
- Evaluate a smaller candidate and a larger candidate against the same acceptance rules.
- A smaller model may handle routine, well-bounded cases while uncertain cases go to a stronger model or person.
Define the difficult part
Model size is an incomplete proxy for capability. Training, task design, context and deployment all affect results. A narrow classifier and an ambiguous research assignment ask different things of a system. Begin with examples of the actual work, including cases where an incorrect answer would be costly.
Compare on your workload
Evaluate a smaller candidate and a larger candidate against the same acceptance rules. Measure valid answers, abstentions, latency and total operating cost. Include the work spent correcting outputs. A fast response that needs repeated retries can lose its apparent advantage.
Route deliberately
A smaller model may handle routine, well-bounded cases while uncertain cases go to a stronger model or person. Test that routing decision too: a weak uncertainty signal can quietly send difficult work down the cheap path. Revisit the split when the task distribution changes.
Sources & notes
An editorial decision framework, not a scored benchmark or hands-on test.
developers.openai.com — official reference
developers.openai.com — official reference
Sources reviewed for the September 2026 launch edition.