Alibaba's Qwen3.7-Max AI Model Achieves 35 Hours of Autonomous Execution

The AI industry has fully entered the "agent era," a paradigm where AI models do far more than generate text — they now actively plan, execute, and course-correct complex tasks over days rather than seconds. Thus, it's perhaps unsurprising to see Chinese e-commerce giant Alibaba's famed Qwen Team of AI researchers release a model capable of performing autonomous agentic AI work over multiple days: that model has arrived in the form of Qwen3.7-Max which the company reports in a blog post achieved "~35 hours of continuous autonomous execution" — albeit, in a proprietary, not open source format, as prior Qwen Team releases were. This is also to be expected — it's what many analysts and industry experts feared in the wake of the departure of several key Qwen Team leaders earlier this year.

But it makes sense for Alibaba financially, at least in the short term: training AI models, especially ones as powerful as Qwen3.7-Max, is expensive, and giving them away essentially for free, as open source models are, does not immediately help recoup any costs. In that sense, Alibaba is simply aligning its efforts with American AI giants like OpenAI and Google by offering the latest and greatest models only through paid APIs and subscription or paid web plan bundles, and slightly less performant ones through open source. Still, the arrival of Qwen3.7-Max offers further optionality to enterprises and individual users, and more competition for American AI labs — rarely a bad thing for consumers at all budget levels.

Yet, the fact that the model is only accessible from Chinese-based endpoints means it may be limited in its appeal to American and European enterprises seeking to maximize compliance and security posturing when fulfilling government contracts, or even just attempting to comply with all relevant state, local, and national data sovereignty regulations. The marathon AI era To understand why Qwen3.7-Max is a departure from previous models, one must look at how it was trained and how it operates in practice. Language models typically degrade when forced to maintain a single train of thought over thousands of conversational turns; they forget instructions, hallucinate variables, or simply get stuck in logical loops.

Qwen3.7-Max was specifically designed as a "versatile agent foundation" capable of "long-horizon reasoning" to overcome this exact bottleneck. The starkest demonstration of this capability is an autonomous engineering task detailed by the Qwen team. The model was given access to an isolated server equipped with a T-Head ZW-M890 PPU—a hardware architecture the model had never encountered during its training.

Its task was to optimize an attention kernel. Over the course of 35 straight hours, Qwen3.7-Max operated entirely autonomously. It executed 1,158 distinct tool calls, performed 432 kernel evaluations, diagnosed compilation failures, and iteratively improved the code to achieve a 10.0x geometric mean speedup.