The ambiguity in comparing those model outputs highlights an important point in this discussion: You'll need a labeled dataset of ground truth on which to test the quality of the model outputs. You could probably construct this by gathering a bunch of comments known to be relevant (zapped more than once, by trusted users, etc), and a bunch of comments known to be LLM/spam. Then test the model's ability to pick out the spam from the relevant.

I'd also probably reduce the dimensionality of the assignment to make the classification task simpler: just relevant yes/no and LLM yes/no is where I'd start.