A personal benchmark is a growing set of tasks from your real work, judged by your own standards. Say you ask an AI to critique an article opening and it misses that the headline is weak. Your correction becomes a check: did the model catch the weak headline? Run that task across models, and you can see which ones meet your standards, regardless of who made them. As you correct more work, the benchmark becomes a better guide to which model is best for you—and when it's time to switch.