davidlee:

I know the right one is more trained and yields better accuracy but,
if the features are more entangled, is there any chance to be more overfitted?


I think your general statement is true.
If the features (in the high dimensional feature space) are more entangled, then the model would have a harder time to separate the classes.
My only concern is how to capture this “entanglement”.
While t-SNE can certainly create your nice plots, I’m always skeptical about the chosen parameters for the t-SNE call.
If you are using sklearn.manifold.TSNE, are you using the default arguments and are other arguments always yielding a comparable output for both models?
That being said, I don’t want to discourage you using t-SNE, as it’s a great technique, and would recommend to also use the losses to describe the overfitting of a model.