Performance does indeed depend on workload. There's a page that compares GraalPy vs CPython and Jython on the Python Performance Suite which aims to be "real world":
https://www.graalvm.org/latest/reference-manual/python/Perfo...
There the speedup is smaller, but this is partly because a lot of real world Python workloads these days spend all their time inside C or the GPU. Having a better implementation is still a good idea though, because it means more stuff can be done by researchers who don't know C++ well or at all. The point at which you're forced to get dedicated hackers involved to optimize gets pushed backwards if you can rely on a good JIT.