It is well documented that pure network problems can be solved from 10 to 100 times faster using specialized primal simplex software as compared to general linear programming systems. For multi-commodity network flow problems, the computational savings are a function of the number of tight-side constraints. In this study, we present three real-world multicommodity models and data concerning the number of tight-side constraints. We also present the results of a computational study on a set of 25 randomly generated test problems which have a wide range of number of tight-side constraints. We conclude that a specialized multicommodity network code is three times as fast as a general code, while a specialized network with general side constraints code has twice the speed of a general LP code.