Note that Python tends to get around this by providing a wide variety of fast builtin functions to do stuff that would otherwise be slow. $ time python -c

Note that Python tends to get around this by providing a wide variety of fast builtin functions to do stuff that would otherwise be slow. $ time python -c 'sum = 0 > for i in range(1000000000): sum += 1' real 0m56.655s user 0m56.646s sys 0m0.008s Compare to: time python -c 'sum(range(1000000000))' real 0m8.693s user 0m8.684s sys 0m0.008s In reality, anyone is going to do heavy arithmetic using Numpy anyway: $ time python -c 'import numpy as np; np.sum(np.arange(1000000000))' real 0m2.022s user 0m1.661s sys 0m2.061s As a nice bonus, you get hardware speed but it still gives you the right answer, unlike Node trying to use limited precision primitive types.