This paper presents an in-depth evaluation of “MCsiNet,” an AI-based feedback channel state information compression and reconstruction model, in a 5G massive MIMO OFDM system, adhering to 3GPP standards using the “CDL-C” MIMO channel model. “M-CsiNet” demonstrates significant improvements over legacy schemes, such as Type-II and Enhanced Type-II. Furthermore, “M-CsiNet” delivers optimal performance in terms of link-level block error rate (BLER) and throughput with 10-15 dB lower SNR, along with a two-order reduction in overhead and a one-order reduction in complexity. These results position “M-CsiNet” as a highly efficient solution for capacity-limited scenarios in both rank-1 and rank-2 cases.