As September kicked off just three days ago, a flurry of five state-of-the-art AI models have been rolled out one after another. Among these, Meta's Muse Spark 1.3 made its debut shortly on the heels of Google's Gemini 3.8 Flash. This new model has demonstrated exceptional prowess across multiple benchmark tests, outperforming counterparts like GPT-5.6 Sol and Claude Opus 5. Notably, Muse Spark 1.3 brings about a significant reduction in the number of tool calls, token usage, and overall costs, making it an incredibly cost-effective option. Under the adept leadership of Alexandr Wang, the model has undergone rapid iterations with the goal of empowering personal agents that operate around the clock. However, Muse Spark 1.3 has not escaped scrutiny, with some critics alleging that it has been specifically tailored to excel in benchmark tests without a corresponding enhancement in its knowledge base. At present, as the benefits derived from increasing model parameters in the large model industry start to wane, the future competitive landscape is expected to hinge on agent engineering and the capability to execute long-term tasks.