An unattributed simulation assessment report reveals that the hybrid GPT-6 Astra framework stands out prominently, securing a score of 62.6 in an embodied intelligence assessment and achieving a groundbreaking architectural-level dimensionality reduction feat. This framework merges the semantic judgment refinement prowess of GPT-6 Astra with the physical interaction action priors of π₀.₅, creating a synergistic system. The assessment underwent validation through two experimental setups: RoboDojo and RoboLab. In RoboDojo's 10 intricate task categories, GPT-6 Astra's direct control success rate was a mere 26%. However, when paired with π₀.₅, the success rate soared to 48%, with an average score climbing to 62.6. Astra predominantly rectified actions at pivotal moments, underscoring the substantial synergy between the two models. In RoboLab's semantic comprehension tasks, GPT-6 Astra's direct control zero-shot success rate soared to 98%, illustrating its robust generalization capabilities, though this outcome merely indicates the upper echelon of its potential. GPT-6 Astra shines in semantic understanding, visual recognition, task planning, and error rectification, enabling it to construct a closed-loop observation-decision-execution cycle and delve into novel strategies. Nevertheless, it falls short in physical interaction tasks like delicate contact and steady grasping, domains where π₀.₅'s action priors effectively bridge this gap. However, the current solution entails exorbitant reasoning costs and has only undergone simulation testing, remaining a considerable distance from real-world deployment. The report underscores that the cognitive generalization abilities of large models have expanded into the embodied intelligence domain, yet they cannot entirely supplant the physical interaction capabilities of embodied models. The fusion of both represents a viable approach, with the wellspring of capabilities rooted in data and computational prowess. In the face of OpenAI's multi-billion-dollar investment, China's embodied intelligence must leverage its rich application scenarios and industrial chains, transforming these scenarios into high-caliber data to bolster model capabilities.