Cython is actually what is faster than Julia in Wes' comparison, not Python. Cython looks kinda, sorta like Python, but it is actually a static language with C-like types (but quite different syntax for those types), no polymorphism, and, afaict, ill-defined semantics. The best answer I seem to get about Cython's semantics is that Cython's semantics are whatever it does. I'm not alone in this complaint – Travis Oliphant expressed a similar concern at this year's SciPy (in this panel [http://www.youtube.com/watch?v=7i2vhoQY-K4], if I recall correctly), which is part of his motivation to work on Numba [https://github.com/numba/numba].

If you look at the comments on Wes' post, when I used the dot(x,y) function, which ships with Julia and uses a BLAS to compute the inner product just like the fastest "Python" version does, Julia is equally fast. That stands to reason – they're both just calling a BLAS.

Finally, that blog post is months old – since then Julia passed the milestone of being no slower than 2x C++ on its microbenchmarks suite [http://julialang.org/]. That's not a guarantee that all code is that fast, but most things we see can be pretty easily tweaked to get there (counterintuitively for those coming from Matlab, Python or R, usually by devectorizing the code rather than vectorizing it). And of course, there's a lot of room for improving Julia's performance, the compiler is still quite young and there are many optimizations that we haven't implemented. Basically, there's nothing but work standing in the way of reaching C or Fortran's speed across the board.