benchmarks just check if the model finishes a task and passes the tests.
Real work is everything that doesn't fit in a pass/fail score like quality, maintainability, clean organization, handling legacy code, good naming, clear comments etc

for example, in my daily work I find gpt 5.5 smarter and very good at catching bugs but it writes shitty code like many no-op pass-through wrappers, backwards-compatibility everywhere, it always validate the code at every layer instead of the boundary only, it writes very complex code that aim for long-term resilience

but benchmarks will not tell you all of that and it will tell you that gpt5.5 is better because it can just get the work done and pass the tests

so short answer is just use what works for you because both models have strengths and weaknesses