A model’s response to correction can matter more than its first answer.

For real work, test whether the system can absorb specific feedback inside the task and produce a cleaner second pass without reintroducing old mistakes. A strong first answer is pleasant, but most business work includes revision. The more important question is whether the model can preserve what was right, fix what was wrong, and respect a correction consistently across the rest of the task. That behavior often predicts day-to-day usefulness better than a flashy first response.