I understand all that (though it's useful to cover it for folks who might not know). But, is Python faster? It carries some baggage of its own. I guess PyPy may be able to make some sorts of problems faster, and it was still nascent at the tail end of when I was using Python; I think it existed, but was nothing more than a proof of concept. Even now, I think it's a pretty limited subset of Python deployments that use PyPy.
And, you speak of numerics, which is a good example of Python's wheel house. When I worked in Python it was at Enthought (the company run by the folks who created Numeric, SciPy, and who sponsors a lot of scientific computing Python projects). Perl definitely doesn't have the numbers performance of a hybrid Python+Fortran or Python+C++ system, like you can build with Python, Numeric, SciPy, etc.
So, even back then, Python was (probably) faster than Perl in that category (Perl has science and bioinformatics libraries, which may have been comparable back then, but certainly aren't now), and I guess it's unfair to compare Perl's strength to Python's weakness, and vice versa.
Nonetheless, I suspect Perl is not slower than Python in the general case. One would probably have to talk about specific tasks and implementations to figure it out.
