GLM-5.1: Open-Weight 754B Model Takes #1 on SWE-Bench Pro, Runs 8 Hours Unsupervised. MIT Licensed.

An open-weight model just beat every closed frontier model on the benchmark builders actually care about.

Z.AI (formerly Zhipu AI) dropped GLM-5.1, a 754-billion parameter mixture-of-experts model with 40 billion active parameters. The SWE-Bench Pro score: 58.4%. That's above GPT-5.4, above Claude Opus 4.6, above Gemini 3.1 Pro. An MIT-licensed model you can download and run just took the top spot.

But the benchmark number isn't the headline. Simon Willison tested it and the result was something I haven't seen from any model. He gave it a single prompt: build a Linux-style desktop environment as a web application. No starter code, no mockups. The model ran for eight hours autonomously. Eight hours of planning, experimenting, reading results, hitting blockers, and pushing through them. Over 600 iterations and thousands of tool calls with maintained goal alignment throughout.

That's not code generation. That's a sustained engineering session.

The practical specs: 95.3 on AIME 2026, 86.2 on GPQA-Diamond, 200K context window, 131K max output tokens. At 40B active parameters, it's tractable on high-end consumer hardware. Think 2-3x 4090s. vLLM had a tagged image within 20 minutes of release. The model is 1.51TB on HuggingFace and available right now.

Reddit's r/LocalLLaMA lit up with 604 upvotes and people immediately pairing it with Nous Research's Hermes Agent framework. The open-source agent stack now has a model that can hold context and execute tasks over hours, not minutes.

My take: the 8-hour autonomous execution is the bigger deal than the benchmark score. SWE-Bench measures one-shot problem solving. Real engineering requires sustained attention, course correction, and the willingness to back up and try a different approach. GLM-5.1 does that. If you're running local models for agent workloads, this should be your first evaluation target.