Benchmark instances for multicommodity flow problems frequently lack the structural nuances of real-world networks or fail to maintain a rigorous mathematical relationship with their single-commodity counterparts. This paper introduces a formal meta-generation framework that addresses these limitations by lifting single-commodity minimum-cost flow instances into the multicommodity space while strictly preserving the underlying network topology, capacity constraints, and nodal roles. To systematically investigate the effect of commodity heterogeneity on computational methods, we propose two distinct integer partitioning schemes: Uniform, which is characterized by homogeneous distributions, and Spread, which introduces high degrees of heterogeneity. Central to this approach is the definition of the (relative) Single-Multi-Commodity Gap (r)SMCG, a novel metric that quantifies the divergence in optimal objective values arising from this lifting process. Numerical experiments demonstrate that the SMCG is significantly influenced by commodity heterogeneity and serves as an indicator of the computational difficulty of multicommodity instances.