Open model releases have turned into a weekly event. Another checkpoint, another chart, another round of confident replies under it — and somewhere in that noise I'm supposed to decide whether this thing deserves a slot in my daily workflow. For a long time my process was: open a chat, throw it a prompt I half-remember, and let my mood write the review.
That process failed me more than once. A model I dismissed after one bad answer turned out fine. A model I adopted on a good first impression hallucinated its way through a real refactor. The common thread: I was reacting, not measuring.
So I built a scoring loop that fits in a coffee break and produces a written verdict instead of a feeling. This post walks through the whole thing — the task file, the runner, the scoring discipline — with code you can lift directly.
Why first impressions lie
Three biases make casual testing useless for model selection:
