Big disclaimer: my experiences are limited, and compiler comparisons are going to be highly variable, depending on the particular code bases.

That said, I have a statistics model that has to be fit thousands (millions?) of times. As a baseline, running it using JAGS (a popular tool for Gibbs sampling) for a given number of iterations and chains in parallel took >160 seconds.

The same model in Julia took 520 ms, g++ & gfortran 480 ms, and ifort took 495 ms.

The code I compiled with g++ used vector intrinsics and SLEEF. Not exactly a revelation that code like that will be fast. But part of my point is that C++ easily lets you take measures like that when you want more performance or control, therefore it is more optimizable. Switching to a commercial compiler wasn't an automatic performance boon.

All else equal, I'd much prefer sticking with an open source compiler suite. I also won't be a student with access to the Intel compilers for much longer.

The actual model is addressing a fun problem (improving the accuracy of satellite-space debris collision probability estimates), and I'm organizing some of it into a "Gibbs Sampling" presentation for tomorrow, so I could put the material online.