I would not think think there is a “you can do X in A but it’s 100% impossible in B”.
Of course, there are plenty of people having all sorts of opinions on PyTorch vs. Tensorflow or fastai (the library from fast.ai) vs. Keras, but I think many most people are just expressing their style preference.
How would you measure better? One way could be to look at benchmarks (e.g. https://dawn.cs.stanford.edu/benchmark/ 25 , I think https://mlperf.org/ 16 will have something during/after NeurIPS, so in a few days) and see what kind of models people use for competing there). Another popular measure is “what do research papers use”, I’m sure we’ll see something about that for NeurIPS, too.
Best regards
Thomas