{"id":60258,"date":"2026-04-07T22:48:52","date_gmt":"2026-04-07T17:18:52","guid":{"rendered":"https:\/\/officechai.com\/?p=60258"},"modified":"2026-04-07T22:48:55","modified_gmt":"2026-04-07T17:18:55","slug":"z-ai-glm-5-1-benchmarks-swe-bench-pro","status":"publish","type":"post","link":"https:\/\/officechai.com\/ai\/z-ai-glm-5-1-benchmarks-swe-bench-pro\/","title":{"rendered":"China&#8217;s Z.AI Releases GLM-5.1, Beats All US Models On SWE-Bench Pro"},"content":{"rendered":"\n<p>A Chinese model is now best in the world at a crucial coding benchmark.<\/p>\n\n\n\n<p>Z.AI, the Beijing-based lab formerly known as Zhipu AI, has released GLM-5.1 \u2014 and its headline number is hard to ignore. The model scored 58.4 on SWE-Bench Pro, the industry&#8217;s toughest software engineering evaluation, clearing GPT-5.4 (57.7), Claude Opus 4.6 (57.3), and Gemini 3.1 Pro (54.2). It is the first Chinese model to top the SWE-Bench Pro leaderboard, and it does so while running on zero Nvidia hardware.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">The SWE-Bench Pro Result<\/h2>\n\n\n\n<p>SWE-Bench Pro tests models on complex, real-world GitHub issues \u2014 the kind of multi-file bugs and system-level refactors that distinguish capable coding agents from autocomplete engines. A 58.4 puts GLM-5.1 roughly a full point ahead of GPT-5.4 and 1.1 points ahead of Claude Opus 4.6. In a field where frontier models are separated by fractions, that margin is meaningful.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" width=\"640\" height=\"347\" src=\"https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-23-1024x555.png?resize=640%2C347&#038;ssl=1\" alt=\"\" class=\"wp-image-60259\" srcset=\"https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-23.png?resize=1024%2C555&amp;ssl=1 1024w, https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-23.png?resize=300%2C163&amp;ssl=1 300w, https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-23.png?resize=768%2C416&amp;ssl=1 768w, https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-23.png?resize=1536%2C832&amp;ssl=1 1536w, https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-23.png?resize=2048%2C1110&amp;ssl=1 2048w, https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-23.png?w=1280 1280w, https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-23.png?w=1920 1920w\" sizes=\"(max-width: 640px) 100vw, 640px\" \/><figcaption class=\"wp-element-caption\">GLM 5.1 benchmarks: Performance on SWE Bench Pro<\/figcaption><\/figure>\n\n\n\n<p>Z.AI describes GLM-5.1 as a post-training upgrade to GLM-5 \u2014 same 744B-parameter Mixture-of-Experts architecture (40B active per token), same 200K context window, retargeted reinforcement learning pipeline aimed specifically at coding distributions. The base GLM-5 <a href=\"https:\/\/officechai.com\/ai\/z-ais-glm-5-displaces-kimi-2-5-thinking-to-become-top-open-model-scores-higher-than-gemini-3-pro-on-artificial-analysis-intelligence-index\/\">had already established itself<\/a> as the first open model to score 50+ on the Artificial Analysis Intelligence Index, beating Gemini 3 Pro. GLM-5.1 pushes that further.<\/p>\n\n\n\n<p>According to Z.AI, the model can run autonomously for up to eight hours, refining strategies across thousands of iterations \u2014 a capability the company calls &#8220;long-horizon agentic engineering.&#8221;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Coding Dominance Across the Board<\/h2>\n\n\n\n<p>SWE-Bench Pro is not a one-off outlier. GLM-5.1&#8217;s coding strength extends across multiple benchmarks.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"640\" height=\"536\" data-src=\"https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-24-1024x858.png?resize=640%2C536&#038;ssl=1\" alt=\"\" class=\"wp-image-60260 lazyload\" data-srcset=\"https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-24.png?resize=1024%2C858&amp;ssl=1 1024w, https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-24.png?resize=300%2C251&amp;ssl=1 300w, https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-24.png?resize=768%2C643&amp;ssl=1 768w, https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-24.png?resize=1536%2C1286&amp;ssl=1 1536w, https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-24.png?w=1662&amp;ssl=1 1662w, https:\/\/i0.wp.com\/officechai.com\/wp-content\/uploads\/2026\/04\/image-24.png?w=1280 1280w\" data-sizes=\"(max-width: 640px) 100vw, 640px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 640px; --smush-placeholder-aspect-ratio: 640\/536;\" \/><figcaption class=\"wp-element-caption\">Z.ai GLM 5.1 benchmarks<\/figcaption><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>NL2Repo (42.7):<\/strong> Top score among all models listed, ahead of Claude Opus 4.6&#8217;s 49.8 and GPT-5.4&#8217;s 41.3 \u2014 this benchmark tests a model&#8217;s ability to generate entire repository structures from natural language descriptions.<\/li>\n\n\n\n<li><strong>Terminal-Bench 2.0 (63.5 on Terminus-2 \/ 66.5 with Claude Code harness):<\/strong> Top-3 globally. Terminal-Bench evaluates agents completing long, multi-step shell tasks with real execution environments.<\/li>\n\n\n\n<li><strong>CyberGym (68.7):<\/strong> The highest score among listed models, well ahead of Claude Opus 4.6 (66.6) and DeepSeek-V3.2 (17.3). CyberGym tests cybersecurity reasoning under adversarial conditions.<\/li>\n<\/ul>\n\n\n\n<p>The NL2Repo and CyberGym results are particularly notable \u2014 they test very different ends of the software engineering spectrum, and GLM-5.1 leads on both.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Agentic Performance<\/h2>\n\n\n\n<p>Beyond raw coding, GLM-5.1 performs strongly across agentic benchmarks \u2014 tasks that require sustained multi-step reasoning, tool use, and goal tracking:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>BrowseComp (68.0 \/ 79.3 with context management):<\/strong> Top open-model score, trailing only proprietary systems on the context-managed variant.<\/li>\n\n\n\n<li><strong>MCP-Atlas (71.8):<\/strong> Top score overall, ahead of Qwen3.6-Plus (74.1) and Claude Opus 4.6 (73.8) \u2014 this tests multi-step tool invocation across real APIs.<\/li>\n\n\n\n<li><strong>\u03c4\u00b3-Bench (70.6):<\/strong> Competitive with GPT-5.4 (72.9) and ahead of Claude Opus 4.6 (72.4).<\/li>\n\n\n\n<li><strong>Vending Bench 2 ($5,634):<\/strong> GLM-5.1 runs a simulated vending business across a full simulated year and finishes with the second-highest balance, behind Claude Opus 4.6&#8217;s $8,017. Vending Bench 2 is one of the few benchmarks that directly proxies economic decision-making under uncertainty.<\/li>\n<\/ul>\n\n\n\n<p><a href=\"https:\/\/officechai.com\/ai\/chinas-ai-models-replace-uss-ai-models-as-open-model-of-choice-for-first-time\/\">Chinese open models<\/a> have increasingly been dominant on agentic tasks \u2014 GLM-5.1 continues that pattern.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Where US Models Still Lead<\/h2>\n\n\n\n<p>The picture is not uniformly in GLM-5.1&#8217;s favor. On <strong>reasoning benchmarks<\/strong>, US and other frontier models hold an edge:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>HLE (31.0):<\/strong> Claude Opus 4.6 scores 36.7, Gemini 3.1 Pro reaches 45.0, and GPT-5.4 hits 39.8.<\/li>\n\n\n\n<li><strong>GPQA-Diamond (86.2):<\/strong> Behind Gemini 3.1 Pro (94.3), GPT-5.4 (92.0), and Claude Opus 4.6 (91.3).<\/li>\n\n\n\n<li><strong>AIME 2026 (95.3):<\/strong> Trailing GPT-5.4 (98.7) and Gemini 3.1 Pro (98.2).<\/li>\n<\/ul>\n\n\n\n<p>These gaps suggest GLM-5.1&#8217;s engineering is deliberately targeted \u2014 the RL pipeline has been optimized for practical coding and agentic execution, not pure mathematical reasoning. It&#8217;s a tradeoff that reflects Z.AI&#8217;s explicit positioning around &#8220;Agentic Engineering&#8221; rather than general-purpose intelligence.<\/p>\n\n\n\n<p>Anthropic CEO Dario Amodei has <a href=\"https:\/\/officechai.com\/ai\/chinese-ai-models-are-optimized-for-benchmarks-instead-of-real-world-use-anthropic-ceo-dario-amodei\/\">previously argued<\/a> that Chinese models tend to be benchmark-optimized and distilled from US labs. Whether that critique applies to GLM-5.1&#8217;s SWE-Bench Pro result specifically will depend on independent verification \u2014 Z.AI&#8217;s benchmarks are self-reported, though its prior SWE-Bench Verified scores for GLM-5 held up well under third-party testing.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">The Hardware Story<\/h2>\n\n\n\n<p>There is a dimension here that goes beyond AI performance. GLM-5.1 was trained entirely on <strong>Huawei Ascend 910B chips<\/strong> using Huawei&#8217;s MindSpore framework \u2014 no Nvidia, no AMD, no American silicon. Zhipu AI has been on the US Entity List since January 2025, effectively barred from acquiring US-manufactured accelerators. The result is a model that tops a key global benchmark despite operating entirely outside the Western AI hardware stack.<\/p>\n\n\n\n<p>Z.AI <a href=\"https:\/\/officechai.com\/ai\/chinese-ai-startup-z-ai-set-to-become-first-ai-model-company-to-go-public-in-hong-kong-ipo\/\">completed a Hong Kong IPO in January 2026<\/a>, raising approximately $558 million USD, and the capital is visibly accelerating its release cadence: GLM-5 launched February 11, GLM-5-Turbo on March 15, and GLM-5.1 on March 27 \u2014 three significant model updates in six weeks.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">Pricing and Access<\/h2>\n\n\n\n<p>GLM-5.1 is available to all GLM Coding Plan subscribers, with plans starting at $3\/month (promotional) and a standard rate from $10\/month. API access is priced at $1.00\/M input tokens and $3.20\/M output tokens. For context, Claude Max runs $100\u2013200\/month.<\/p>\n\n\n\n<p>Z.AI has confirmed GLM-5.1 will be open-sourced, though no timeline has been set. The GLM-5 base model is already available on HuggingFace under an MIT license.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">The Bigger Picture<\/h2>\n\n\n\n<p>With <a href=\"https:\/\/officechai.com\/ai\/80-chance-that-startups-we-see-are-using-chinese-ai-models-andreessen-horowitz-partner\/\">80% of startups now gravitating toward Chinese open models<\/a> according to Andreessen Horowitz data, GLM-5.1&#8217;s SWE-Bench Pro result arrives at a moment when the competitive stakes couldn&#8217;t be higher. Software engineering is the benchmark that matters most to enterprise AI buyers \u2014 it&#8217;s where models either earn their place in production pipelines or don&#8217;t.<\/p>\n\n\n\n<p>GLM-5.1 just made a compelling case.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A Chinese model is now best in the world at a crucial coding benchmark. Z.AI, the Beijing-based lab formerly known as Zhipu AI,&#8230;<\/p>\n","protected":false},"author":1,"featured_media":60259,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1029],"tags":[],"class_list":["post-60258","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>China&#039;s Z.AI Releases GLM-5.1, Beats All US Models On SWE-Bench Pro<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/officechai.com\/ai\/z-ai-glm-5-1-benchmarks-swe-bench-pro\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"China&#039;s Z.AI Releases GLM-5.1, Beats All US Models On SWE-Bench Pro\" \/>\n<meta property=\"og:description\" content=\"A Chinese model is now best in the world at a crucial coding benchmark. 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