A personal benchmark is a set of tasks drawn from your real work, with checks that capture what a good result looks like to you. Those checks come from your judgment and the corrections you make when AI gets something wrong. For an editor, a task might be giving feedback on an article opening, with checks like: Does it push the headline to make the strongest possible claim? Does it suggest concrete wording? Running different models on the same tasks shows which ones meet your standards. As new models come out, your benchmark gives you a way to tell whether they’re better for the work you actually do, whoever makes them.