Machine Learning: if test, validation errors are same, the model with lower train loss is better at generalization? I tuned some hyperparameters and got 2 best models.

These two models are almost the same "test(not train)" and "validation" errors. But model A has lower "train" error than model B.

In this case, the model with lower "train" error (model A), would show good performance on new data? (good generalization)

Or model A is more overfitting to the dataset so it would work worse in new data? 

Thanks
Though usually the test error is your primary goal, I can imagine some specific cases when the train error also matters. One possible example: training set has much more 'hard' inputs than the test set.

If you ever visually examined MNIST, you've probably seen that some images are puzzling even for a human, while others are much easier to tell. If one would split the training/test set differently, it's possible to see that training error would be higher than the validation or test error.

However, it's important to say that a situation like that indicates that data is probably poorly collected or split. So in this case it's better not to choose model A vs model B, but to check the data.