Abstract The treatment of induced seismicity risk would benefit from a small, standardized injection test that probes the seismic response to fluid injection—to optimally improve earthquake forecasts before a larger injection commences. To accomplish this goal, we propose a ‘model differencing’ framework: this framework optimizes injection protocols by maximizing forecast differences between three model paradigms (statistics‐based, physics‐based, and machine‐learning‐based). The resulting injection protocols exploits modeling assumptions, to maximize differences in predictions. These optimized protocols are two‐phased: first forcing one model to be relatively more productive and then flipping it to be relatively under productive in the second phase. These phases create forecast differences by exploiting differences in model assumptions. We synthesize these results into a prototype for the SFIT (Seismogenic Fault Injection Test). With further refinement, our SFIT prototype could develop into a standardized test for induced seismicity. We further discuss model differencing to construct testable hypotheses.