Like Brian said, the Xeon Phi cores are not at all comparable to the CUDA ones. The problem with the Phi is that it's somewhere between two horses.
If you are doing highly parallel floating point calculations, NVIDIA will provide you with something like 3 times the performance at 1/4th of the price. For double precision the gap is smaller, but NVIDIA still ends up being 20% cheaper for the same performance.
If your problem is very hard to parallelize, the Phi will not help you at all and instead something like an Intel Xeon will give you the best performance.
The sweet spot for the Phi is then something highly parallel, but divergent, i.e. each thread has to do something different. An example of this would be Monte Carlo simulations. They are for instance used in simulations of radiotherapy treatments, where GPUs only give a small (~2x) speed-up over a standard CPU.
Intel is also trying to sell the the Xeon Phi on the fact that you only need to rewrite your code minimally. However, for anything that is not trivial to parallelize, the work becomes the same as for a GPU.