Yes it depends on what you're trying to do. If you're looking for something to automate part of your composition and make it an "algorithmic" piece where the computer picks the notes, these models are just too limited for that, at least so far.
BTW, "counterpoint" generally refers to one facet of how Western music works, the process of setting "note against note" in musical lines (or "voices") that preserve some kind of autonomy. But there's many other things that explain what makes music sound good, both within a single line and on a broader view, where music is written to target "rest points" or "key areas", and repeat or develop "thematic" material.
(The model I pointed to above doesn't even try in the least to explore these broader-scale things, it's trained on a very small-scale view of its input. It deals pretty well with counterpoint, and the inner workings of a single line. It ends up doing interesting things nonetheless when trying to make sense of its music as it randomly drifts out of the established scale - ISTM that it sometimes ends up changing key as a result of alterations in melody, not always in the background harmony. One could see this as a kind of very light and subtle atonality, even as part of what's clearly a 'tonal' style. It also knows about different historical styles within tonal music, and manages to overlay and transition between them quite well IMHO.)
