I think the main difference would be the last linear layer and the corresponding weight matrix.
While you would use a single output neuron and a sigmoid activation for nn.BCELoss, you would use two outputs for nn.CrossEntropyLoss. This would basically double the number of parameters in this layer.
If you think your results are too good, I would recommend to check for some data leak, as this might be more likely that the small difference between the criteria.