Purely anecdotal, but my experience has been that LLMs generate low quality python code. It's spaghetti code on par with what I've seen when companies I worked at tried offshoring development. It's basically what you get when you give bad or incomplete specs to a team of inexperienced programmers with poor development practices. It's usually good in small chunks, but it gets extremely sloppy as the scope of work increases and more decisions are introduced. Interestingly, I've seen LLMs generate good clojure code.

My guess is it comes down the the training data more than anything else, although I suspect functional languages will fare a little better. At least that's been my experience. There's undoubtedly a ton of python code in the training corpus and portions of it are of dubious quality. Niche functional languages likely have a smaller training corpus where a larger portion of it is better quality.