The intuitive explanation often given -that smaller models generalize better-turns out to be fallacious, since the training method for creating embeddings makes no reference to analogy solving.
there is no a priori reason why lowdimensional model parameters (i.e., lower model capacity) should lead to better performance in analogy solving, just as there is no reason they are better at some other unrelated task like predicting the weather.