This was ages ago, in the pre-transformer era, and I can't find the link anymore. But once upon a time I read a great paper that demonstrated that most of the performance differences being reported among popular embedding models of the time were better explained by text cleaning and tokenization than they were by the embedding model itself.

In other words, if you train a model using word2vec's preprocessing and GloVe's algorithm, the result looks more like a "standard-issue" word2vec model than a "standard-issue" GloVe model.