Electrocatalysts enable the efficient conversion of molecules for applications in energy devices, but due to their material stability, the electrochemical performance tends to change over time under operating conditions. For the oxygen reduction reaction (ORR), transition metal x-ides (oxides, nitrides, sulfides) are a class of highly tunable, low-cost catalysts being considered as possible alternatives to expensive Pt-based materials. In this work, we take a multimodal characterization approach to investigate manganese antimony (MnSb) oxide, nitride, and sulfide nanoparticles for the ORR and characterize their performance in both acidic and alkaline conditions. X-ray photoelectron spectroscopy and transmission electron microscopy confirm that the three materials are of comparable morphology and polycrystallinity while having distinct elemental compositions. In pH 13 electrolyte, the nitride demonstrates a ∼40 mV higher ORR onset potential (at −0.1 mA cm −2 geo ) than both the oxide and sulfide and has nearly 100% selectivity toward H 2 O. In comparison, in pH 1 electrolyte, the nitride and sulfide are ∼300 mV higher in ORR onset (at −0.1 mA cm −2 geo ) than the oxide and both exhibit greater selectivity to H 2 O than the oxide. In situ Mn K-edge synchrotron X-ray absorption reveals that, despite significant material changes to the MnSb sulfide and nitride under electrochemical conditions, their Mn-oxidation state and ligand environment do not converge to those found in the MnSb oxide. Furthermore, online inductively coupled plasma−mass spectrometry of the materials elucidates distinct mechanisms of continuous dissolution and nanoparticle detachment that are functions of pH, potential, and material composition. Measuring changes in complex, nonprecious materials under dynamic electrochemical operation conditions provides insight into how an as-synthesized material can transform into a distinct active catalytic species. Moreover, correlating orthogonal online/ operando/in situ measurement techniques in similar catalyst operation conditions is a powerful method for resolving mechanistic information about degradation and performance changes.