Population heterogeneity is one of the basics of the diffusion models at the individual level; although its importance is well known, there is a lack of knowledge about the impact of the technique to operationalize this heterogeneity. This paper evaluates the impact of three techniques for operationalizing heterogeneity in modeling the diffusion of innovations at the individual level: (1) modeling one-to-one, (2) homogeneous group modeling, and (3) heterogeneous group modeling. An agent-based diffusion model was developed and the impact of each technique was evaluated on three variables: diffusion, adoption intention, and computational requirements. The input data for the model came from 230 people surveyed on the intention to adopt an innovation. As a conclusion, it was mainly observed that in homogeneous groups, the techniques present significant differences in the model results and marginal differences in the computational requirements. Therefore, the technique for representing agent heterogeneity in modeling diffusion phenomena at the individual level is not a trivial component in models, and its choice must be deliberate.