Abstract: The pursuit of general-purpose embodied agents is currently hindered by fragmented evaluation protocols that isolate navigation skills and fixate on specific robot morphologies. This disconnect fails to reflect real-world scenarios where agents must orchestrate diverse behaviors across varying physical embodiments. To bridge this gap, we introduce OmniNavBench, a holistic benchmark designed to rigorously assess cross-skill coordination and cross-embodiment generalization. Distinguished from existing datasets, OmniNavBench introduces three paradigm shifts: (1) Compositional Complexity. We propose composite instructions that interleave sub-tasks from 6 distinct categories (i.e., PointNav, VLN, ObjectNav, SocialNav, Human Following and EQA), compelling agents to seamlessly transition between exploration, interaction, and social compliance within a single unified episode. (2) Morphological Universality and Sensor Flexibility. We present a simulation platform that breaks the reliance on single-morphology evaluation. This ecosystem empowers researchers to test generalization across different robot types, including humanoid, quadrupedal, and wheeled, while accommodating diverse algorithmic needs through a modular sensor interface and a hybrid suite of 170 environments blending synthetic assets with real-world scans. (3) Demonstrations Quality. Moving beyond mechanical shortest-path algorithms, we curate 1,769 expert trajectories via human teleoperation, capturing critical behavioral nuances, such as exploratory glance and anticipatory avoidance, essential for natural human-robot coexistence. Extensive evaluations demonstrate that current methods, despite their claimed unified design, struggle to adapt to the complex, interleaved nature of truly general-purpose navigation. This exposes a critical disparity between existing capabilities and the demands of real-world deployment, underscoring OmniNavBench as a crucial testbed for the next generation of generalist navigators. Dataset will be released.