Whether your not the cosine similarity of either pair is higher depends on the mapping you create from the strings to the embedding vector. That mapping can be whichever function you choose, and your result will be entirely dependent on that.

If you choose a straight linear mapping of tokens to a number, then you'd be right.

Extending that, if you choose any mapping which does not do a more extensive remapping from raw syntactic structure to some sort of semantic representation, you'd be right.

But hence why we increasingly use models to create embeddings instead of simpler approaches before applying a similarity metric, whether cosine similarity or other.

Put another way, there is no inherent reason why you couldn't have a model where the embeddings for 1 and 3 are identical even, and so it is meaningless to talk about the cosine similarity of your sentences without setting out your assumptions about how you will created embeddings from them.