See the other replies above, but: the logistic has heavier tails than the normal, so might do better in cases where we need robustness, where unexpected outcomes remain possible even in cases where the linear predictor is relatively big, and we want to avoid drawing extreme inferences from them.
Probit might lead to more efficient inferences in cases where the mechanism is known to become deterministic relatively quickly as the linear predictor gets big.
You could go further in either direction too (more or less robust) by using other link functions.
Probit might lead to more efficient inferences in cases where the mechanism is known to become deterministic relatively quickly as the linear predictor gets big.
You could go further in either direction too (more or less robust) by using other link functions.