Not the parent, but the imperative interface supported by the dynamic graph approach Pytorch takes is much nicer.
Additionally, in my personal opinion Tensorflow is often too low level and Keras is often too high level for the things I'm trying to do for research. While you can jump between the two of course, I think PyTorch hits a much more natural middle ground in its API.
Tensorflow/Keras is making improvements in these areas with the eager execution, and is still great for putting models into production, but I think PyTorch is much better for doing research or toying with new concepts.
No hard benchmarks, just personal experience. Note that I’m not saying regular Tensorflow is slower than pytorch (in fact I’ve found them to be roughly the same) just eager mode.
Edit: Just realized this might be a good thing to write a blog post about. I’ll get back to you after finals :)