The only drawback is that you have the decompression time when starting the software, but in my case, the customers have not noticed it yet.
Edit: The advantages of Nuitka are that as you compile your code to C++, you make it faster and also protect it against easy reverse engineering. This could be interesting to compile the critical parts of your application with Nuitka and pack the rest with PyInstaller.
As Loic states, Nuitka works differently from cx_Freeze in that Nuitka takes your Python source, compiles that to C++, then compiles the C++, whereas cx_Freeeze creates Python bytecode which is subsequently run by the included Python interpreter.
The result should be that the same Python code should run much faster in the form created by Nuitka, than the result created by cx_Freeze.
How do you figure? Python's slowness is not due to it's lack of compilation, it's due to its dynamic nature and all of the runtime lookups.
Past efforts to compile python down to bytecode have not resulted in speedups. Unladen Swallow is one such failed example. PyPy gets around this by analyzing the actual running code and is only able to speed up a subset of all of Python.
It is not compiling down to Python bytecode.
It is compiling to an operational-equivalent C++ source code.
Then it is compiling that C++ code to executable object code.