Same validation accuracy, different train accuracy for two neural networks models
Based on this, both models generalize equally well. However both have overfit, the second one more significantly. You simply want to avoid that. For example, I'd expect the best validation loss to be closer to training loss if you're using early stopping. You can turn up things like dropout.

If you do so, I think you'll find the augmented model ends up producing a better model (lower validation loss).