Gensim save_word2vec_format() vs. model.save()
EDIT: this was intended as a comment. Don't know how to change it now, sorry
correlation between the word occurrence-frequency and vector-length I don't quite follow - aren't all your vectors the same length? Or are you not referring to the embedding vectors?
I am using gensim version 0.12.4 and have trained two separate word embeddings using the same text and same parameters. After training I am calculating the Pearsons correlation between the word occurrence-frequency and vector-length.  One model I trained using save_word2vec_format(fname, binary=True) and then loaded using load_word2vec_format the other I trained using model.save(fname) and then loaded using Word2Vec.load(). I understand that the word2vec algorithm is non deterministic so the results will vary however the difference in the correlation between the two models is quite drastic. Which method should I be using in this instance?