Retest on your own cases first. A new model may handle hard work better, but published speed and quality claims don’t measure your workflow.
Isn’t the newest model always better?
It’s better on someone’s tests. Yours include your documents, your instructions and your awkward cases.
1. Retry what fell short.
Keep the cases where the current model struggled. They are the fastest way to see whether anything changed.
2. Run the same set on both.
Use known good cases, past failures and exceptions, with the same instructions. Compare accepted results, corrections, time and cost.
A fictional comparison.
- Cases
- 20, the same for both.
- Current model
- 14 accepted.
- New model
- 17 accepted, but one case that was right before is now wrong.
- Next step
- Look into that case before switching.
Practical check: A better total can still hide a new mistake.
3. Check what else changes.
Price per use, limits, data terms and who can get access.
4. Switch like any other change.
Get the owner’s approval and keep a way back to the previous model.
Read more: Maintaining an AI Agent: What to Do When Things Change · Why an AI Agent Works in the Demo but Fails on Real Work
The example is fictional. This is general guidance, not advice for your situation.