I took its preliminary findings into Claude Code with the same model. But in mine it knows where every adjacent system is, the entire git history, deployment history, and state of the feature flags. So instead of pointing at a vague problem, it knew which flag had been flipped in a different service, see how it changed behavior, and how, if the flag was flipped in prod, it'd make the service under testing cry, and which code change to make to make sure it works both ways.
It's not as if a modern Opus is a small model: Just a stronger scaffold, along with more CLI tools available in the context.
The issue here in the security testing is to know exactly what was visible, and how much it failed, because it makes a huge difference. A middling chess player can find amazing combinations at a good speed when playing puzzle rush: You are handed a position where you know a decisive combination exist, and that it works. The same combination, however, might be really hard to find over the board, because in a typical chess game, it's rare for those combinations to exist, and the energy needed to thoroughly check for them, and calculate all the way through every possible thing. This is why chess grandmasters would consider just being able to see the computer score for a position to be massive cheating: Just knowing when the last move was a blunder would be a decisive advantage.
When we ask a cheap model to look for a vulnerability with the right context to actually find it, we are already priming it, vs asking to find one when there's nothing.