One of the most useful concepts in the study of biochemical reaction networks is flux. These networks are often modeled by systems of ordinary differential equations. The variables represent concentrations of chemicals, and the equations describe how the reactions affect the concentrations. ODE models of reaction networks are deterministic continuous approximations of reaction networks. A newer and more accurate class of models represents the state of a network as a discrete population of molecules. Reactions are discrete random events whose probability depends on the numbers of each type of molecule in the population. Recently Kahramanoğulları proposed a definition of flux for these classes of models. We will show how the classic definition of flux for ODE models is derived from the definition of flux for discrete stochastic models. The two versions of flux will be compared on some examples. We will conclude with suggestions for possible applications of discrete stochastic flux and some open problems.