I think Python and R are generally superior (in terms of developer experience) when you have to do end-to-end ML work, including acquiring and munging data, plotting results etc. But even then, the core algorithms are generally implemented in libraries built out of native code (C/C++/Fortran), just wrapped in friendly bindings.

For LLMs, unless you're doing extensive work refactoring the inputs, there are fewer productivity gains to be had around the edges - the main gains are just speeding up training, evaluation and inference, i.e. pure performance.