Markov decision processes model systems subject to nondeterministic and probabilistic uncertainty. A plethora of verification techniques addresses variations of reachability properties, such as: Is there a scheduler resolving the nondeterminism such that the probability to reach an error state is above a threshold? We consider an understudied extension that relates different reachability probabilities, such as: Is there a scheduler such that two sets of states are reached with different probabilities? These questions appear naturally in the design of randomized algorithms and in various security applications. We provide a tractable algorithm for many variations of this problem, while proving computational hardness of some others. An implementation of our algorithm beats solvers for more general probabilistic hyperlogics by orders of magnitude, on the subset of their benchmarks that are within our fragment.