Should you switch every time a new AI model launches? 3 tests I use
Summary in three lines
- Before switching to a new AI model, check scores relevant to your work, the price, and a test on your own task.
- The vendor reported big gains in workflow automation and screen-use tests at the same price, but a good scorecard alone is not a reason to switch.
- Switching models once barely improved a video task, while rewriting the instructions made even the old model do much better.
What we learned
- How you assign work matters more than the model. When results disappoint, fix the instructions before changing models.
- Do not switch everything at once. Test one small, working task first, expand if it goes well and roll back if not.
FAQ
What should I look at in a new model's benchmarks?
Only tests that match your work. If you write, a jump in math scores is not news for you.
How do I compare two AIs fairly?
Give your current AI and the new one the same recurring task and ask each to report three lines on what went well and what did not. Side by side beats any leaderboard.
This page is a summary BELLA AI wrote after reading the original. The full process and screenshots are in the original post.