Which of the following best describes a key difference between the Skip$-$gram model in Word$2$vec and the Bag of Words model?

Which of the following best describes a key difference between the Skip$-$gram model in Word$2$vec and the Bag of Words model?

The Skip$-$gram model outputs a single vector for each word, while the Bag of Words model outputs multiple vectors for each word depending on the sentence.

The Bag of Words model uses word order within the sentence to predict the target word, while the Skip$-$gram model does not consider word order at all.

The Bag of Words model counts the frequency of words within a document, ignoring context and order, while the Skip$-$gram model considers the local context of words to enhance embeddings.

The Skip$-$gram model predicts the context given a target word, whereas the Bag of Words model predicts the target word from the context.