GLM-5.1
First open-source model to top SWE-bench Pro — 744B MoE, MIT, zero Nvidia
The Panel's Take
GLM-5.1 is Z.ai's (formerly Zhipu AI) open-weight model released April 7, 2026 under the MIT license. It's a 744-billion-parameter Mixture-of-Experts architecture with 40 billion active parameters per token, a 200K-token context window, and a 131K maximum output length — and it became the first open-source model ever to lead SWE-bench Pro, scoring 58.4% versus Claude Opus 4.6's 57.3%. The training story is almost as remarkable as the performance. GLM-5.1 was trained entirely on approximately 100,000 Huawei Ascend 910B chips using the MindSpore framework — no Nvidia hardware was used at any point. That makes it one of the first frontier-tier models to demonstrate that the CUDA monoculture isn't technically mandatory for training state-of-the-art models. Z.ai became the first publicly traded foundation model company via a Hong Kong IPO in January 2026 (~$558M raised). The model is free to download from HuggingFace and also available via API at $0.95 per million input tokens. In agentic demonstrations, it has run autonomously for eight hours straight — 655 planning and execution iterations — without human checkpoints.
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GLM-5.1 verdict: SKIP ⏭️ 2 ships · 2 skips from the expert panel Full review: shiporskip.io/tool/glm-5-1-zai-744b-moe-open-source-swebench-pro-top-huawei-mit-2026
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“MIT license, top SWE-bench Pro score, $0.95/M via API. If your use case is agentic coding and you're not evaluating GLM-5.1, you're leaving real performance on the table. The 8-hour autonomous run capability is compelling for long-horizon task pipelines.”
“SWE-bench Pro is one benchmark. The broader coding composite (Terminal-Bench 2.0 + NL2Repo) still has Claude Opus 4.6 ahead at 57.5 vs GLM-5.1's 54.9. Running 744B locally requires hardware most teams don't own, and the API's Chinese jurisdiction will trigger compliance blockers for many organizations.”
“The Huawei chip training story matters more than the benchmark ranking. If GLM-5.1 proves you can train frontier models without Nvidia at scale, it fractures the GPU supply chain narrative that's been shaping geopolitics and AI policy discussions for years. This is a proof of concept with enormous implications.”
“For creative workflows, the 744B MoE overhead is overkill and local deployment requires datacenter-grade hardware that's nowhere near indie studio territory. The MIT license is great, but the gap between 'free to download' and 'free to actually run' is vast at this parameter count.”