You are likely to get better results if you do not use the same model for review that wrote the code. I typically use Opus for code editing and GPT 5.5 for peer review using an automation with skills.
Training set is different between models. If there are gaps in coverage in one model, you want a different model reviewing the work. The second model will its own gaps, but the gap list is not identical.
There’s no evidence of this. I guess you are anthropomorphising models (i.e., it’s good that - different human reviews your code)
However, using two models to generate two reviews easily beats doing one model and one review, as some models seem to "care" more about certain things, but you'll just miss different things if you change the model rather than add more.
Or if you make it "be a security engineer" with particular focus points.
Or make it a grammar nazi, it will find way more typos than without such focus.
Of course all of those "focuses" needs to be in a separate context (agent/subagent) to make it work.
And if you put the review effort into polishing an impl plan, then it doesn't matter which model implements it either.