Usually you would select the model yielding the “best” performance.
However, it seems you are interested in further fine-tuning or optimizing the model, so it’s a bit hard to tell, which model you should select.
Note, that the second model might learn a bit slower due to non-optimal hyperparameters, so you might want to experiment with changing these.
You could train the model as long as the training and validation losses decrease by a significant amount.