Generative AI (GenAI) is becoming a mainstream network workload, yet packet-level simulators lack measurement-driven GenAI traffic models. Currently researchers must approximate GenAI services using traditional sources such as file transfer and video streaming, limiting realistic network evaluation of scheduling and capacity planning. We present MINT, a measurement and modeling framework for GenAI network traffic. Using an isolated network-namespace capture pipeline, we collect client-side traces from three LLM providers across four modalities, cloud and edge servers, and wired and wireless network access points. We find that GenAI modalities exhibit distinct upload/download asymmetry and burst structures that differ from traditional applications. MINT clusters and models these burst regimes and reproduces empirical behavior in ns-3 with normalized Wasserstein distances of 2–25%. Our results also reveal that constant token generator models fail to capture realistic packet burst variability. MINT open-sources the first measurement-driven GenAI traffic model for packet-level network simulation.