SciPy SVD vs. Numpy SVD

Both SciPy and Numpy have built in functions for singular value decomposition (SVD). The commands are basically scipy.linalg.svd and numpy.linalg.svd. What is the difference between these two? Is any of them better than the other one?

From the FAQ page, it says scipy.linalg submodule provides a more complete wrapper for the Fortran LAPACK library whereas numpy.linalg tries to be able to build independent of LAPACK.

I did some benchmarks for the different implementation of the svd functions and found scipy.linalg.svd is faster than the numpy counterpart:

However, jax wrapped numpy, aka jax.numpy.linalg.svd is even faster:

Full notebook for the benchmarks are available here.