AI tool comparison
GLM-5.1 vs GLM-5.1
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
AI Models
GLM-5.1
First open-source model to top SWE-bench Pro — 744B MoE, MIT, zero Nvidia
50%
Panel ship
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Community
Paid
Entry
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.
AI Models
GLM-5.1
#1 on SWE-Bench Pro — 744B MoE model that runs autonomously for 8 hours
50%
Panel ship
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Community
Paid
Entry
GLM-5.1 is Z.AI's post-training upgrade of the 744B Mixture-of-Experts GLM-5 model, and it has just claimed the top spot on SWE-Bench Pro with a score of 58.4 — beating GPT-5.4 (57.7), Claude Opus 4.6 (57.3), and Gemini 3.1 Pro (54.2). The model is designed for long-horizon agentic tasks and can run autonomously for up to 8 hours across thousands of iterations on a single problem. The agentic capabilities include extended context retention, tool-calling with recovery loops, and a reinforcement-trained "persistence" mode that keeps the model on-task through failures and dead ends rather than surfacing errors to the user. The model was trained entirely on Huawei Ascend 910B chips using the MindSpore framework — no US silicon, no CUDA. The geopolitical dimension is as significant as the technical one: GLM-5.1 is direct evidence that US export controls on Nvidia hardware have not meaningfully slowed China's frontier model development. The 8-hour autonomous execution window is also a step-change from current agentic systems that struggle past 20-30 minutes of coherent work — if this benchmark holds up in real-world testing, it's a genuine advancement in the class of problems AI agents can independently solve.
Reviewer scorecard
“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.”
“If the 8-hour autonomous execution claim is real and not cherry-picked, this changes the calculus for using AI on genuinely hard engineering problems. SWE-Bench Pro #1 is also a credible metric — I want to test this on my own repos immediately.”
“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.”
“SWE-Bench benchmarks have historically shown poor correlation with real-world coding productivity, and the '8-hour autonomous' claim needs independent validation. Z.AI is also a relatively unknown quantity compared to Anthropic or Google — API reliability and pricing are completely unproven.”
“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.”
“The strategic significance of a Chinese lab hitting #1 on the coding benchmark using zero US hardware cannot be overstated. The export control strategy is officially not working as intended, and GLM-5.1 will accelerate the geopolitical AI arms race in ways that reshape the entire industry.”
“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.”
“For creative work, I need a model with strong multimodal capabilities and reliable API access — both unproven for GLM-5.1. The coding benchmark lead is impressive but not directly relevant to my workflows. I'll wait for independent reviews before switching.”
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