Without a way to tune it, this visualization is as much about the dimensionality reduction algorithm used as the embeddings themselves, because trade-offs are unavoidable when you go from a very high dimensional space to a 2D one. I would not read too much into it.
This demo is a lot more useful for comparing word embeddings: https://www.cs.cmu.edu/~dst/WordEmbeddingDemo/index.html
You can choose which dimensions to show, pick which embeddings to show, and play with vector maths between them in a visual way
It doesn't show the whole set of embeddings, though I am sure someone could fix that, as well as adapting it to use the gpt-oss model instead of the custom (?) mini set it uses.