numpy.random.randint slower than random.randint

In [27]: import random as rnd

In [28]: from numpy import random as nrnd

In [29]: %timeit rnd.randint(0,5)
859 ns ± 22.8 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [30]: %timeit nrnd.randint(0,5)
4.53 µs ± 104 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [31]: %timeit nrnd.randint(0,10000000)
4.67 µs ± 116 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [32]: %timeit rnd.randint(0,10000000)
979 ns ± 25.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

Python 3.7.3 (default, Mar 27 2019, 16:54:48)
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In my 2nd test case, I have increased the random range to (0,10000000). As the range is larger, numpy should perform better at this test case.

Why is numpy.random.randint() taking more time than built in random.randint? What extra operations is numpy doing here?