{"id":128725,"date":"2025-12-24T16:36:54","date_gmt":"2025-12-24T08:36:54","guid":{"rendered":"https:\/\/vertu.com\/?p=128725"},"modified":"2025-12-24T16:38:11","modified_gmt":"2025-12-24T08:38:11","slug":"glm-4-7-vs-claude-opus-4-5-the-thinking-open-source-challenger","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-vs-claude-opus-4-5-the-thinking-open-source-challenger\/","title":{"rendered":"GLM-4.7 vs. Claude Opus 4.5: The &#8220;Thinking&#8221; Open Source Challenger"},"content":{"rendered":"<h1 data-path-to-node=\"2\"><\/h1>\n<p data-path-to-node=\"3\"><b data-path-to-node=\"3\" data-index-in-node=\"0\">Is the reign of expensive proprietary models ending?<\/b> The release of <b data-path-to-node=\"3\" data-index-in-node=\"68\">GLM-4.7<\/b> by Z.AI has sent shockwaves through the developer community, with early benchmarks and user reports suggesting it might finally be the open-source rival to Anthropic's flagship <b data-path-to-node=\"3\" data-index-in-node=\"253\">Claude Opus 4.5<\/b>.<\/p>\n<p data-path-to-node=\"4\">While official charts primarily target <b data-path-to-node=\"4\" data-index-in-node=\"39\">Claude Sonnet 4.5<\/b> and <b data-path-to-node=\"4\" data-index-in-node=\"61\">GPT-5<\/b>, community discussions on Reddit are telling a bolder story: specifically in agentic coding and &#8220;thinking&#8221; tasks, GLM-4.7 is punching far above its weight class\u2014and doing it for a fraction of the cost.<\/p>\n<p data-path-to-node=\"5\">Here is the definitive breakdown of how GLM-4.7 stacks up against the current king of reasoning.<\/p>\n<hr data-path-to-node=\"6\" \/>\n<h2 data-path-to-node=\"7\">1. The Core Difference: &#8220;Thinking&#8221; Architecture<\/h2>\n<p data-path-to-node=\"8\">The defining feature of GLM-4.7 is its rigorous implementation of <b data-path-to-node=\"8\" data-index-in-node=\"66\">&#8220;Thinking&#8221; capabilities<\/b>, which directly challenges the reasoning superiority usually reserved for Opus-class models.<\/p>\n<ul data-path-to-node=\"9\">\n<li>\n<p data-path-to-node=\"9,0,0\"><b data-path-to-node=\"9,0,0\" data-index-in-node=\"0\">Interleaved Thinking:<\/b> Unlike standard models that rush to answer, GLM-4.7 &#8220;thinks&#8221; before every response and tool call. This mimics the chain-of-thought process that makes Claude Opus 4.5 so effective at complex instruction following.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"9,1,0\"><b data-path-to-node=\"9,1,0\" data-index-in-node=\"0\">Preserved Thinking:<\/b> This is the game-changer for agents. In multi-turn conversations (like debugging a long error log), GLM-4.7 retains its &#8220;thought blocks&#8221; across the entire session. It doesn't just remember <i data-path-to-node=\"9,1,0\" data-index-in-node=\"209\">what<\/i> was said; it remembers <i data-path-to-node=\"9,1,0\" data-index-in-node=\"237\">why<\/i> it made previous decisions.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"9,2,0\"><b data-path-to-node=\"9,2,0\" data-index-in-node=\"0\">The Result:<\/b> Users reporting on <i data-path-to-node=\"9,2,0\" data-index-in-node=\"31\">r\/LocalLLM<\/i> noted that for tasks like fixing concurrency issues in Playwright tests, GLM-4.7\u2019s reasoning felt indistinguishable from high-end proprietary models.<\/p>\n<\/li>\n<\/ul>\n<h2 data-path-to-node=\"10\">2. Coding Performance: Benchmarks vs. Reality<\/h2>\n<p data-path-to-node=\"11\">This is where the controversy\u2014and excitement\u2014lies.<\/p>\n<h3 data-path-to-node=\"12\">The Official Numbers<\/h3>\n<p data-path-to-node=\"13\">According to Z.AI\u2019s technical report, GLM-4.7 scores <b data-path-to-node=\"13\" data-index-in-node=\"53\">84.9 on LiveCodeBench V6<\/b>, significantly outperforming <b data-path-to-node=\"13\" data-index-in-node=\"107\">Claude Sonnet 4.5<\/b> (64.0). While it falls slightly behind Opus 4.5 in pure abstract reasoning benchmarks (like MMLU-Pro), the coding metrics are &#8220;insane&#8221; for an open-weight model.<\/p>\n<h3 data-path-to-node=\"14\">The OpenCode CLI Experience<\/h3>\n<p data-path-to-node=\"15\">On <i data-path-to-node=\"15\" data-index-in-node=\"3\">r\/opencodeCLI<\/i>, developers testing GLM-4.7 inside agentic tools (like <b data-path-to-node=\"15\" data-index-in-node=\"72\">OpenCode<\/b> and <b data-path-to-node=\"15\" data-index-in-node=\"85\">Claude Code<\/b>) are reporting results that rival Opus 4.5 in practical workflows.<\/p>\n<ul data-path-to-node=\"16\">\n<li>\n<p data-path-to-node=\"16,0,0\"><b data-path-to-node=\"16,0,0\" data-index-in-node=\"0\">Speed:<\/b> Users describe it as &#8220;faster than 4.6 by a wide margin,&#8221; closer to lightweight models like Kimi or Nemotron, yet with the heavy-lifting logic of a dense model.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"16,1,0\"><b data-path-to-node=\"16,1,0\" data-index-in-node=\"0\">Stability:<\/b> The &#8220;Preserved Thinking&#8221; feature seems to solve the &#8220;lazy dev&#8221; problem where models degrade after 10+ turns.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"16,2,0\"><b data-path-to-node=\"16,2,0\" data-index-in-node=\"0\">The Caveat:<\/b> Some users warn of &#8220;benchmaxxing&#8221;\u2014where a model is over-optimized for tests but feels &#8220;brittle&#8221; in niche real-world scenarios compared to the &#8220;workhorse&#8221; reliability of Sonnet or Opus.<\/p>\n<\/li>\n<\/ul>\n<h2 data-path-to-node=\"17\">3. Barrier to Entry: Hardware vs. Cloud<\/h2>\n<p data-path-to-node=\"18\">The biggest difference between the two is <b data-path-to-node=\"18\" data-index-in-node=\"42\">accessibility<\/b>.<\/p>\n<ul data-path-to-node=\"19\">\n<li>\n<p data-path-to-node=\"19,0,0\"><b data-path-to-node=\"19,0,0\" data-index-in-node=\"0\">Claude Opus 4.5:<\/b> A closed API. It is the most expensive model on the market, offering zero control over privacy or weights. You pay a premium for the &#8220;it just works&#8221; magic.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"19,1,0\"><b data-path-to-node=\"19,1,0\" data-index-in-node=\"0\">GLM-4.7:<\/b> A massive <b data-path-to-node=\"19,1,0\" data-index-in-node=\"19\">360B+ parameter<\/b> beast.<\/p>\n<ul data-path-to-node=\"19,1,1\">\n<li>\n<p data-path-to-node=\"19,1,1,0,0\"><b data-path-to-node=\"19,1,1,0,0\" data-index-in-node=\"0\">Local Use:<\/b> To run this locally (and beat Opus), you need enterprise-grade hardware (multiple H100s or a cluster of consumer GPUs). A single Mac Studio M2 Ultra likely won't cut it without heavy quantization.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"19,1,1,1,0\"><b data-path-to-node=\"19,1,1,1,0\" data-index-in-node=\"0\">API Cost:<\/b> This is the killer app. Z.AI is offering GLM-4.7 via API for roughly <b data-path-to-node=\"19,1,1,1,0\" data-index-in-node=\"79\">1\/7th the price<\/b> of comparable Claude tiers, with a &#8220;Coding Plan&#8221; that undercuts Anthropic aggressively.<\/p>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2 data-path-to-node=\"20\">4. Why Developers Are Switching<\/h2>\n<p data-path-to-node=\"21\">The sentiment on Reddit is shifting from &#8220;curiosity&#8221; to &#8220;practical adoption.&#8221;<\/p>\n<ol start=\"1\" data-path-to-node=\"22\">\n<li>\n<p data-path-to-node=\"22,0,0\"><b data-path-to-node=\"22,0,0\" data-index-in-node=\"0\">Tool Use:<\/b> GLM-4.7\u2019s native ability to handle web browsing (scoring 67 on BrowseComp) and terminal commands makes it a drop-in replacement for Claude in agentic workflows.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"22,1,0\"><b data-path-to-node=\"22,1,0\" data-index-in-node=\"0\">No &#8220;Lazy&#8221; Refusals:<\/b> Unlike the safety-heavy Opus 4.5, which can sometimes refuse complex requests or lecture the user, GLM-4.7 (while still safe) is described as more &#8220;pragmatic&#8221; and willing to execute code.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"22,2,0\"><b data-path-to-node=\"22,2,0\" data-index-in-node=\"0\">Ownership:<\/b> For teams building internal tools, the option to self-host (eventually, with enough compute) offers data security that Anthropic cannot match.<\/p>\n<\/li>\n<\/ol>\n<h2 data-path-to-node=\"23\">Final Verdict: Is it an &#8220;Opus Killer&#8221;?<\/h2>\n<p data-path-to-node=\"24\"><b data-path-to-node=\"24\" data-index-in-node=\"0\">Not yet\u2014but it's close enough that the price difference might not matter.<\/b><\/p>\n<p data-path-to-node=\"25\">If you need the absolute pinnacle of creative writing and nuance, <b data-path-to-node=\"25\" data-index-in-node=\"66\">Claude Opus 4.5<\/b> remains the gold standard. However, for <b data-path-to-node=\"25\" data-index-in-node=\"122\">coding agents, debugging, and systematic reasoning<\/b>, GLM-4.7 has effectively commoditized &#8220;Opus-level&#8221; intelligence.<\/p>\n<p data-path-to-node=\"26\"><b data-path-to-node=\"26\" data-index-in-node=\"0\">Winner for Value:<\/b> GLM-4.7 <b data-path-to-node=\"26\" data-index-in-node=\"26\">Winner for Raw Logic:<\/b> Claude Opus 4.5 (by a hair)<\/p>\n<p data-path-to-node=\"27\"><i data-path-to-node=\"27\" data-index-in-node=\"0\">Ready to test it? You can try GLM-4.7 via the Z.ai platform or pull the weights from HuggingFace if you have the GPU horsepower.<\/i><\/p>","protected":false},"excerpt":{"rendered":"<p>Is the reign of expensive proprietary models ending? The release of GLM-4.7 by Z.AI has sent shockwaves through the developer [&hellip;]<\/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-128725","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\/128725","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=128725"}],"version-history":[{"count":0,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/posts\/128725\/revisions"}],"wp:attachment":[{"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/media?parent=128725"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/categories?post=128725"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/tags?post=128725"}],"curies":[{"name":"\u0648\u0648\u0631\u062f\u0628\u0631\u064a\u0633","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}