Abstract: Network packet processing is a fundamental function of network devices, involving tasks such as packet modification, checksum and hash computation, mirroring, filtering, and packet metering. As a domain-specific processor, Network Processors (NP) can provide line-rate performance and programmability for network packet processing. However, due to different design requirement, architecture of NP differs, including single-phase NP and multi-phase NP, posing challenges for NP designers. Existing simulation methods mainly target single NP or single architecture and are not available to explore both of the architectures. This paper proposes Neptune, an analyzing framework for generic network processor microarchitecture modeling and performance simulation. Based on detailed analysis, Neptune adopts multi-phase NP architecture as the hardware model while providing ability to simulate single-phase architecture. Besides, Neptune employs event list mechanism and inter-core queues to support simulation of different data paths and various scheduling strategies in multi-phase NP. Furthermore, Neptune utilizes bulk synchronous parallel graph computing mechanism and takes advantage of both event-driven and time-driven simulation, ensuring accuracy and efficiency. Our experiment shows that Neptune achieves over 95% accuracy in simulating both of the architectures and simulates network processors at a performance of 3.31MIPS, achieving an order of magnitude improvement over PFPSim. We illustrate the universality and capability of the Neptune simulation framework through three specific cases. Firstly, we evaluated multi-phase and single-phase NP, showing that single-phase NP can achieve up to a 1.167x performance improvement. Secondly, we optimize the packet parsing module using a programmable pipeline and analyze its performance differences. Finally, we use Neptune to test the performance of the packet processing engine under different thread counts, providing insights for software and hardware multi-threading optimization.