For an apples to apples comparison, the area under the ROC (AUC) would be the best metric. This is because the AUC does not depend on the thresholding value. It is also not sensitive to imbalances in the dataset. (Ideally, we should use the same validation/test data to perform comparisons, so one could argue that the dataset imbalance is not such a big deal.)
Depending on the application, the sensitivity or specificity might be more important. For example, you might have high penalties for false negatives, which implies you want high sensitivity but can tolerate some loss in specificity. In such cases, it will make sense to check what is the best sensitivity (or specificity) you can achieve, this can be obtained from the ROC (the complete curve). The AUC might be misleading, in such cases.
For alternate metrics, you could also consider the Precision-Recall (Recall = Sensitivity) curves (see https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118432).
Note however that Precision is affected by data imbalance.
There is some good discussion here: