I have not tested, but I would expect more niche ecosystems like Rust or Haskell or Erlang to have better overall training set (developer who care about good engineering focus on them), and potentially produce the best output.
For C and C++, I'd expect similar situation with Python: while not as approachable, it is also being pushed on beginning software engineers, and the training data would naturally have plenty of bad code.
As languages designed for (and probably written by) AI come out over the next decade, it will be really interesting to see what dragon tradeoffs they make.
I would argue that because Rust is so strict having the agent compile and run tests on every iterations is actually less needed then in other languages.
I program mostly in python but I keep my projects strictly typed with basedpyright and it greatly reduced the amount of errors the agent makes because it can get immediate feedback it has done something stupid.
Of course you still need to review the code because it doesn't solve logic bugs.
I would dare to say that any Lisp (Common Lisp, Clojure, Racket, whatever) is perfect for a tight AI loop thanks to REPL-driven development. It's an interesting space to explore and I know that the Clojure community at least are trying to figure out something there.
Writing quick python scripts works a lot better than niche domain specific code
I have seen these shine on frontend work