Behavioral models of interest in software engineering all have equivalent computational power because they are all Turing-equivalent. They do not, however, all have equivalent modeling power. Modeling power is a matter of expressive efficiency, which is difficult to measure because of its subjectivity. One non-subjective measure of modeling power compares models by investigating their representational succinctness. This paper investigates the modeling power of a newly proposed behavioral model, called state nets. The paper presents state nets, shows how the succinctness concept of modeling power can be useful in comparing new models with existing ones, and, as an example, proves that state nets have more modeling power than Petri nets.