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.