Miles Lubin and Iain Dunning gave an excellent presentation at MIT's IAP tutorial on Julia in January where they benchmarked rather non-trival code for doing linear programming in a variety of languages:

  https://github.com/JuliaLang/julia-tutorial/blob/master/NumericalOptimization/presentation.pdf
  http://bit.ly/VQ9rDd (shortened version since that's cut off)

See page 7, "Simplex Benchmarks". Essentially, they implemented the basics you need to do a fast simplex linear programming algorithm in each of C++, Julia, Matlab and Python. Their results were in line with the Julia home page benchmarks:

  Julia is between 1-2x slower than good C/C++ code.
  PyPy and Matlab are 10-20x slower.
  Python is 80-400x slower.

The code can be found here:

  https://github.com/mlubin/SimplexBenchmarks

These results are fairly typical.