Academic research in 5G networking faces a lack of accessible, realistic packet-level datasets, limiting innovation and reproducibility. This paper evaluates two state-of-the-art machine learning approaches, PAC-GAN and TabularARGN, for generating synthetic 5G TCP/IP packet headers. Using a real 5G packet-capture dataset, we adapt both models to include inter-packet timing and rigorously assess them on protocol validity, marginal distribution alignment, and joint distribution fidelity. Results show that PAC-GAN produces highly valid and statistically faithful synthetic packets, effectively modeling complex header dependencies and temporal patterns. While TabularARGN ensures strict protocol compliance, it struggles to capture higher-order correlations and traffic diversity. Our findings establish convolutional generative models like PAC-GAN as practical tools for producing realistic, protocol-compliant synthetic 5G traffic, broadening access to datasets for benchmarking and security testing.