Technically, yes, a teraflop is a teraflop, and is directly comparable. It just means you can do an awful lot of floating point operations per second. But many systems are sensitive to memory size, memory bandwidth, and as a result communication costs (i.e. latency/bandwidth of the interconnect between machines).

The benchmark is essentially bottlenecked on FP64 matrix multiplies. If that's what you need to do, then sure, it's indicative.

Some machine learning workloads are also bottlenecked on matrix multiply, but don't need FP64 precision. They can use FP16. Fits a bigger model in a given memory size, makes better use of memory bandwidth, and given the right hardware support, you can get extremely high rates as on Pascal.

Personally, I find the memory system on Pascal more interesting than raw flops rate. Also, the use of nvlink to link multiple GPUs..