We introduce OceanGym, the first comprehensive benchmark for underwater embodied agents, designed to advance AI in one of the most demanding real-world environments. OceanGym features eight realistic task domains and a unified agent framework driven by Multimodal Large Language Models (MLLMs) that integrates perception, memory, and sequential decision-making. Agents must follow natural language instructions, interpret optical and sonar inputs, autonomously explore complex environments, and achieve long-horizon goals. Extensive experiments reveal a significant performance gap between state-of-the-art MLLM-driven agents and human experts, underscoring persistent challenges in perception, planning, and adaptation underwater. By offering a high-fidelity, rigorously designed platform, OceanGym establishes a testbed for robust embodied AI and represents a foundational step toward sim-to-real transfer on Autonomous Underwater Vehicles (AUVs), marking progress toward agents capable of operating in one of Earth’s last unexplored frontiers.