I linked to the website (which was updated in May, but its contents could do with more work) because it has examples of how well the suite fits together.

I don't know much about Jax. I've seen competent benchmarks showing an order of magnitude benefit for using ReverseDiff from the AutoDiff suite over Autograd, which is what Pytorch uses for reverse-mode autodiff

It was update in May to fix that the CDN changed. The actual content hasn't been updated in 7 years.

It really should be updated to have basically the content of this thread https://discourse.julialang.org/t/state-of-automatic-differe...