Alibaba's Qwen3.7-Max Runs 35 Hours Straight to Hit a 10x Coding Speedup

Alibaba's Qwen team has released Qwen3.7-Max, the new flagship of the Qwen3.x family and the most capable model the team has shipped to date. It is a proprietary, API-only model with a 1 million token context window, built explicitly for agentic workloads , autonomous coding, long-horizon task execution, tool orchestration, and office automation. It is now available through Alibaba Cloud Model Studio and third-party aggregators like OpenRouter.

The headline number is 35 hours

The most striking demonstration in the release is a 35-hour autonomous kernel optimization run. The Qwen team handed the model a GPU code optimization problem, gave it a way to test its own output, and stepped back. Over 1,158 tool calls, Qwen3.7-Max rewrote, tested, and iterated on the code , achieving a 10x geometric mean speedup over the Triton reference implementation, on hardware it had never encountered before.

The model was still finding meaningful improvements past the 30-hour mark. For comparison, GLM-5.1 Thinking maxed out at 7.3x, Kimi K2.6 at 5.0x, and DeepSeek V4 Pro at 3.3x. This is the kind of result that is hard to dismiss: it is concrete, verifiable, and it directly demonstrates the property the team is claiming , sustained long-horizon reasoning without context loss or performance regression.

What makes it different from prior Qwen models

Qwen3.7-Max was trained using what Alibaba calls environment scaling , the agentic equivalent of training on more diverse text. Rather than optimizing for a single benchmark scaffold, the training infrastructure decouples each instance into three independent components: Task, Harness, and Verifier. These can be freely recombined, forcing the model to learn generalizable problem-solving strategies rather than scaffold-specific shortcuts.