{"id":128722,"date":"2025-12-24T16:35:21","date_gmt":"2025-12-24T08:35:21","guid":{"rendered":"https:\/\/vertu.com\/?p=128722"},"modified":"2025-12-24T16:35:21","modified_gmt":"2025-12-24T08:35:21","slug":"glm-4-7-released-a-deep-dive-into-z-ais-new-coding-reasoning-powerhouse","status":"publish","type":"post","link":"https:\/\/legacy.vertu.com\/ar\/%d9%86%d9%85%d8%b7-%d8%a7%d9%84%d8%ad%d9%8a%d8%a7%d8%a9\/glm-4-7-released-a-deep-dive-into-z-ais-new-coding-reasoning-powerhouse\/","title":{"rendered":"GLM-4.7 Released: A Deep Dive into Z.ai\u2019s New Coding & Reasoning Powerhouse"},"content":{"rendered":"

<\/h1>\n

The landscape of Artificial Intelligence has shifted once again with the release of GLM-4.7<\/strong>. Positioned as a major leap forward in “Advancing the Coding Capability,” this new model from Z.ai<\/strong> (Zhipu AI) introduces significant improvements in agentic coding, complex reasoning, and tool usage.<\/p>\n

For developers, data scientists, and enterprise users, the question is simple: How does GLM-4.7 stack up against its predecessor, GLM-4.6, and the current titans of the industry like Gemini 3 Pro and Claude Sonnet 4.5?<\/p>\n

In this review, we break down the key features of GLM-4.7, analyze its “Vibe Coding” capabilities, and provide detailed benchmark comparisons to help you decide if it\u2019s the right engine for your next project.<\/p>\n

What is GLM-4.7? Key Features at a Glance<\/h2>\n

GLM-4.7 isn't just a minor patch; it is a substantial upgrade focused on making AI a more effective partner in complex workflows. According to the official Z.ai technical report<\/a>, the model excels in three core areas:<\/p>\n

    \n
  1. Core Coding & Agents:<\/strong> GLM-4.7 is designed to think before it acts. It supports Interleaved Thinking<\/strong> and Preserved Thinking<\/strong>, allowing it to maintain context across multi-turn coding sessions. This results in a massive 12.9% boost on SWE-bench Multilingual and a 16.5% boost on Terminal Bench 2.0.<\/li>\n
  2. Vibe Coding (UI Quality):<\/strong> Beyond logic, GLM-4.7 understands aesthetics. It generates cleaner, modern webpages with better layouts, magnetic CTAs, and accurate sizing\u2014moving away from generic “AI-generated” looks.<\/li>\n
  3. Complex Reasoning:<\/strong> With a 12.4% increase in performance on the HLE (Humanity's Last Exam)<\/strong> benchmark, the model demonstrates a superior ability to solve difficult mathematical and logic problems compared to GLM-4.6.<\/li>\n<\/ol>\n

    Comparison 1: GLM-4.7 vs. GLM-4.6 (The Upgrade)<\/h2>\n

    The most immediate comparison for current users is against the previous version. GLM-4.7 offers clear gains across the board, particularly in tasks requiring external tools and complex instruction following.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n
    Benchmark Category<\/th>\nMetric \/ Dataset<\/th>\nGLM-4.7<\/strong><\/th>\nGLM-4.6<\/strong><\/th>\nImprovement<\/strong><\/th>\n<\/tr>\n
    Reasoning<\/strong><\/td>\nHLE (Humanity's Last Exam)<\/td>\n24.8%<\/strong><\/td>\n17.2%<\/td>\n+7.6%<\/td>\n<\/tr>\n
     <\/td>\nHLE (w\/ Tools)<\/td>\n42.8%<\/strong><\/td>\n30.4%<\/td>\n+12.4%<\/td>\n<\/tr>\n
     <\/td>\nAIME 2025 (Math)<\/td>\n95.7%<\/strong><\/td>\n93.9%<\/td>\n+1.8%<\/td>\n<\/tr>\n
    Coding Agents<\/strong><\/td>\nSWE-bench Verified<\/td>\n73.8%<\/strong><\/td>\n68.0%<\/td>\n+5.8%<\/td>\n<\/tr>\n
     <\/td>\nSWE-bench Multilingual<\/td>\n66.7%<\/strong><\/td>\n53.8%<\/td>\n+12.9%<\/td>\n<\/tr>\n
     <\/td>\nTerminal Bench 2.0<\/td>\n41.0%<\/strong><\/td>\n24.5%<\/td>\n+16.5%<\/td>\n<\/tr>\n
    General Agents<\/strong><\/td>\nBrowseComp<\/td>\n52.0%<\/strong><\/td>\n45.1%<\/td>\n+6.9%<\/td>\n<\/tr>\n
     <\/td>\n\u03c4\u00b2-Bench (Tool Use)<\/td>\n87.4%<\/strong><\/td>\n75.2%<\/td>\n+12.2%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Data Source: Z.ai GLM-4.7 Technical Report (2025)<\/em><\/p>\n

    Analysis:<\/strong> The jump in Terminal Bench 2.0 (+16.5%)<\/strong> and HLE w\/ Tools (+12.4%)<\/strong> indicates that GLM-4.7 is significantly better at handling real-world environments where the AI needs to execute commands, browse the web, or use specific APIs to solve a problem.<\/p>\n

    Comparison 2: GLM-4.7 vs. The Giants (Gemini 3 Pro, Claude Sonnet 4.5, GPT-5.1)<\/h2>\n

    How does GLM-4.7 compete on the global stage? The following table compares it against the heavy hitters: Gemini 3.0 Pro<\/strong>, Claude Sonnet 4.5<\/strong>, and GPT-5.1 High<\/strong> (referred to here as Pro\/High tier).<\/p>\n

    While GLM-4.7 may not win every single metric, it proves to be a highly competitive alternative, especially in reasoning-heavy tasks where it often outperforms Claude Sonnet 4.5 and rivals the GPT-5 series.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n
    Benchmark<\/th>\nGLM-4.7<\/strong><\/th>\nGemini 3.0 Pro<\/strong><\/th>\nClaude Sonnet 4.5<\/strong><\/th>\nGPT-5.1 High<\/strong><\/th>\n<\/tr>\n
    MMLU-Pro<\/strong> (Reasoning)<\/td>\n84.3<\/td>\n90.1<\/strong><\/td>\n88.2<\/td>\n87.0<\/td>\n<\/tr>\n
    GPQA-Diamond<\/strong> (Expert QA)<\/td>\n85.7<\/td>\n91.9<\/strong><\/td>\n83.4<\/td>\n88.1<\/td>\n<\/tr>\n
    HLE w\/ Tools<\/strong> (Complex)<\/td>\n42.8<\/strong><\/td>\n45.8<\/td>\n32.0<\/td>\n42.7<\/td>\n<\/tr>\n
    AIME 2025<\/strong> (Math)<\/td>\n95.7<\/strong><\/td>\n95.0<\/td>\n87.0<\/td>\n94.0<\/td>\n<\/tr>\n
    HMMT Feb 2025<\/strong> (Math)<\/td>\n97.1<\/td>\n97.5<\/strong><\/td>\n79.2<\/td>\n96.3<\/td>\n<\/tr>\n
    LiveCodeBench-v6<\/strong> (Code)<\/td>\n84.9<\/td>\n90.7<\/strong><\/td>\n64.0<\/td>\n87.0<\/td>\n<\/tr>\n
    SWE-bench Verified<\/strong> (Eng)<\/td>\n73.8<\/td>\n76.2<\/td>\n77.2<\/strong><\/td>\n76.3<\/td>\n<\/tr>\n
    Terminal Bench 2.0<\/strong><\/td>\n41.0<\/td>\n54.2<\/strong><\/td>\n42.8<\/td>\n47.6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Note: “GPT-5.1 High” data is used for the GPT-5.1 comparison. “-” indicates data not available in the source.<\/em><\/p>\n

    Key Takeaways<\/h3>\n
      \n
    1. Math & Reasoning Parity:<\/strong> In the AIME 2025<\/strong> benchmark, GLM-4.7 (95.7%) actually outperforms Gemini 3.0 Pro (95.0%) and GPT-5.1 High (94.0%), demonstrating world-class mathematical reasoning capabilities.<\/li>\n
    2. Competitive Tool Use:<\/strong> On the HLE (w\/ Tools)<\/strong> benchmark, GLM-4.7 scores 42.8%<\/strong>, effectively tying with GPT-5.1 High (42.7%) and beating Claude Sonnet 4.5 (32.0%) by a wide margin. This suggests GLM-4.7 is an excellent choice for agentic workflows involving complex problem-solving.<\/li>\n
    3. Coding Efficiency:<\/strong> While Gemini 3.0 Pro leads in raw coding benchmarks like LiveCodeBench, GLM-4.7 remains a strong contender, particularly given its optimization for “Vibe Coding” (UI\/Frontend generation), which benchmarks don't always capture fully.<\/li>\n<\/ol>\n

      Why “Vibe Coding” Matters<\/h2>\n

      One of the standout features of GLM-4.7 is “Vibe Coding.” Traditional coding models often produce functional but ugly frontend code. GLM-4.7 has been tuned to produce “cleaner, more modern webpages”<\/strong> right out of the box.<\/p>\n

        \n
      • Better Defaults:<\/strong> High-contrast dark modes, bold typography, and magnetic CTAs.<\/li>\n
      • Less Iteration:<\/strong> Developers spend less time styling “ugly” boilerplate code.<\/li>\n<\/ul>\n

        Getting Started with GLM-4.7<\/h2>\n

        GLM-4.7 is available now via multiple channels:<\/p>\n

          \n
        • Z.ai Platform:<\/strong> Use it directly in the chat interface or via API.<\/li>\n
        • Coding Agents:<\/strong> It is integrated into tools like Claude Code<\/strong>, Kilo Code<\/strong>, and Roo Code<\/strong>.<\/li>\n
        • Local Deployment:<\/strong> Weights are available on HuggingFace<\/strong> and ModelScope<\/strong>, with support for vLLM and SGLang.<\/li>\n<\/ul>\n

          \u062e\u0627\u062a\u0645\u0629<\/h2>\n

          GLM-4.7 represents a maturing of the AI ecosystem. It is no longer just about who has the highest generic score, but who handles tools<\/strong>, complex reasoning<\/strong>, and multilingual coding<\/strong> best. With its ability to outperform major competitors in mathematical benchmarks like AIME 2025 and its focus on high-quality UI generation, GLM-4.7 is a model that demands attention in 2025.<\/p>","protected":false},"excerpt":{"rendered":"

          The landscape of Artificial Intelligence has shifted once again with the release of GLM-4.7. Positioned as a major leap forward […]<\/p>","protected":false},"author":11214,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[468],"tags":[],"class_list":["post-128722","post","type-post","status-publish","format-standard","hentry","category-best-post"],"acf":[],"_links":{"self":[{"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/posts\/128722","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/users\/11214"}],"replies":[{"embeddable":true,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/comments?post=128722"}],"version-history":[{"count":0,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/posts\/128722\/revisions"}],"wp:attachment":[{"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/media?parent=128722"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/categories?post=128722"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/tags?post=128722"}],"curies":[{"name":"\u0648\u0648\u0631\u062f\u0628\u0631\u064a\u0633","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}