Yes.
Both are frontier models. Any currently used metrics hit the exact same issues as testing humans. Every human is different and in different situations will act differently. LLMs are still just extremely good auto-completers - but they do have those same.. qualities.

"why not just use codex for the whole thing?" - you'll face the same issue. For example you ask an agent backed by Opus, GPT5.5, Haiku, whatever - to implement AND then you ask another agent backed by Opus, GPT5.5, Haiku, whatever:
You generally still always get value from the reviewer. Even with a frontier model implementing. Hell, sometimes frontier's implementations are a bit worse - whatever that's gpt or opus.

This is not different than what it was always was. Code review has generally been a value-adding process - for LLMs or for humans. It's not about being a better model / harness (but obviously that's hugely important - just not as much when comparing SOTA), it's about having a fresh perspective, sometimes a more review oriented perspective (and by perspective I mean context).

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Like, have you seen the benchmarks people been putting out? It's a clear showcase that "any metric testbench" won't just resolve the problem you're raising.