Tensorflow's advantage is that once you build your model, you can run on everything from a massive cluster to a mobile GPU without significant modification. Because you're just writing a description of a computation graph, it's easy for backend systems to process that description and optimize the execution of your model.

PyTorch's imperative semantics (where the computation graph is implicitly defined at runtime by the execution of your Python code) definitely make it cleaner to do research prototyping. But AFAIK most PyTorch models need be reimplemented in lower-level code, or maybe something like Caffe2, before they can be used in production. That's a fairly significant tradeoff, which makes it hard to see PyTorch totally replacing Tensorflow anytime in the near future. That said PyTorch is obviously a great tool and it's exciting to see how it will develop and be used.

(disclaimer: I work for Google, opinions are my own)