Traditional benchmarking measures how systems fail. The Mean Conditions for Instability (MCI) methodology measures something far more operationally relevant: the precise conditions under which systems stop behaving predictably. This paper introduces a stability-first benchmarking framework that detects instability onset using reproducible statistical indicators of jitter, nonlinear latency, and resource oscillation. The result is a quantifiable stability envelope that architects and operators can use to define reliable deployment boundaries.