{"id":80756,"date":"2026-06-25T21:02:12","date_gmt":"2026-06-25T18:02:12","guid":{"rendered":"https:\/\/saudishopper.com.sa\/en\/glm-5-2-arc-agi-results\/"},"modified":"2026-06-28T21:02:59","modified_gmt":"2026-06-28T18:02:59","slug":"glm-5-2-arc-agi-results","status":"publish","type":"post","link":"https:\/\/saudishopper.com.sa\/en\/glm-5-2-arc-agi-results\/","title":{"rendered":"GLM 5.2 Achieves Record-Breaking Open-Weight Scores on ARC-AGI Benchmarks"},"content":{"rendered":"<p class=\"wp-block-paragraph\">The newly released GLM 5.2 ARC-AGI benchmark results have set a historic record for open-weight models. Developed by Z.ai, this 753-billion parameter model achieved an impressive 77.0% on ARC-AGI-1 and 22.8% on ARC-AGI-2. These scores represent the highest ever recorded for an open-weight system on these challenging reasoning tests.<\/p><p class=\"wp-block-paragraph\">This achievement highlights how open-source <a href=\"\/category\/ai\/\">artificial intelligence<\/a> is rapidly closing the capability gap with proprietary models. According to a report by <a href=\"https:\/\/officechai.com\/ai\/glm-5-2-scores-77-on-arc-agi-1-22-on-arc-agi-2-highest-for-an-open-model\/\" target=\"_blank\" rel=\"noopener\">Office Chai<\/a>, the model performs comparably to low-reasoning configurations of GPT-5.4 and GPT-5.5.<\/p><h2 class=\"wp-block-heading\">What the GLM 5.2 ARC-AGI Scores Mean<\/h2><p class=\"wp-block-paragraph\">The Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) measures general fluid intelligence. It forces models to solve entirely novel visual grid-based puzzles with minimal prior training. GLM 5.2 achieved its ARC-AGI-1 score at just $0.19 per task, while the more difficult ARC-AGI-2 cost $0.25 per task.<\/p><h2 class=\"wp-block-heading\">Architectural Innovations Behind the Efficiency<\/h2><p class=\"wp-block-paragraph\">To achieve this level of efficiency on modern <a href=\"\/category\/computers\/\">computers<\/a>, Z.ai introduced several key architectural upgrades. The model utilizes an optimization called IndexShare, which shares a single attention index across multiple sparse layers. This reduces per-token compute by roughly three times at long context lengths. Additionally, an upgraded Multi-Token Prediction layer boosts speculative decoding by up to 20% during inference.<\/p><h2 class=\"wp-block-heading\">The Remaining Gap to Frontier Systems<\/h2><p class=\"wp-block-paragraph\">Despite these record-breaking GLM 5.2 ARC-AGI results, a significant gap remains when compared to the absolute frontier. OpenAI&#8217;s GPT-5.5 currently leads the ARC-AGI-2 benchmark with an 85% score, followed closely by GPT-5.4 Pro at 83.3%. GLM 5.2&#8217;s score of 22.8% shows that proprietary systems still hold a massive advantage in complex, novel reasoning tasks.<\/p><h2 class=\"wp-block-heading\">Market Impact and Chinese AI Competitiveness<\/h2><p class=\"wp-block-paragraph\">The release has had an immediate effect on the global tech <a href=\"\/category\/economy\/\">economy<\/a>. Shares of Knowledge Atlas, the publicly listed entity connected to Z.ai, have roughly doubled since the model&#8217;s debut. Furthermore, <a href=\"https:\/\/venturebeat.com\/technology\/z-ais-open-weights-glm-5-2-beats-gpt-5-5-on-multiple-long-horizon-coding-benchmarks-for-1-6th-the-cost\" target=\"_blank\" rel=\"noopener\">VentureBeat<\/a> reports that GLM 5.2 beats GPT-5.5 on multiple long-horizon coding benchmarks for a fraction of the cost, signaling a rapid acceleration in Chinese AI development.<\/p>","protected":false},"excerpt":{"rendered":"<p>GLM 5.2 has set a new record for open-weight models on the ARC-AGI benchmarks, delivering high-level reasoning at a fraction of the cost of proprietary systems.<\/p>\n","protected":false},"author":8,"featured_media":80755,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_meta-robots-noindex":"","_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","_pr_autopilot_social_copy":"{\"x_hook\":\"Z.ai's GLM 5.2 scored 77.0% on ARC-AGI-1 and 22.8% on ARC-AGI-2, setting a historic record for open-weight models at just $0.19 to $0.25 per task.\",\"x_takeaways\":\"Key Highlights:\\n\ud83d\udd39 77.0% score on ARC-AGI-1 at just $0.19 per task\\n\ud83d\udd39 22.8% score on ARC-AGI-2 at $0.25 per task\\n\ud83d\udd39 IndexShare attention reduces per-token compute by 3x\\n\ud83d\udd39 Multi-Token Prediction boosts speculative decoding by 20%\\n\ud83d\udd39 Outperforms GPT-5.5 on long-horizon coding benchmarks\"}","_yoast_wpseo_opengraph_title":"GLM 5.2 Sets New Open-Weight Record on ARC-AGI Benchmarks","_yoast_wpseo_opengraph_description":"Z.ai's GLM 5.2 achieves historic scores on ARC-AGI-1 and ARC-AGI-2, challenging proprietary models at a fraction of the cost.","_yoast_wpseo_opengraph_image":"https:\/\/saudishopper.com.sa\/en\/wp-content\/uploads\/2026\/06\/glm-5-2-arc-agi-benchmark-results.webp","_yoast_wpseo_opengraph_image_id":80755,"_yoast_wpseo_twitter_title":"GLM 5.2 Sets New Open-Weight Record on ARC-AGI","_yoast_wpseo_twitter_description":"Z.ai's GLM 5.2 achieves historic scores on ARC-AGI-1 and ARC-AGI-2, challenging proprietary models.","_yoast_wpseo_twitter_image":"https:\/\/saudishopper.com.sa\/en\/wp-content\/uploads\/2026\/06\/glm-5-2-arc-agi-benchmark-results.webp","_yoast_wpseo_twitter_image_id":80755,"_yoast_wpseo_primary_category":449,"_extra_hide_author_box":"","_extra_featured_post":"0","_partner_post_id":0,"footnotes":""},"categories":[449,446,24],"tags":[2640,1911,4890,1169,1672],"class_list":["post-80756","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-computers","category-economy","tag-ai-benchmarks","tag-arc-agi-2","tag-glm-5-2-2","tag-open-source-ai","tag-z-ai","et-has-post-format-content","et_post_format-et-post-format-standard"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>GLM 5.2 ARC-AGI Record Open-Weight Scores | Saudi Shopper<\/title>\n<meta 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