Z.ai GLM-5.3: Benchmark Results, Cyber Capabilities, and How It Stacks Up Against GPT-5.6 Sol and Mythos 5
On August 14, 2026, Z.ai (formerly Zhipu AI) launched GLM 5.3, a 740 billion parameter open weight model that scores 84.5% on CyberGym — edging past Anthropic's Mythos 5 (83.8%) and OpenAI's GPT 5.6 Sol (83.6%) for th... GLM 5.3 delivered roughly 50% better coding performance than its predecessor and uncovered 2,436...
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On August 14, 2026, Z.ai (formerly Zhipu AI) launched GLM 5.3, a 740 billion parameter open weight model that scores 84.5% on CyberGym — edging past Anthropic's Mythos 5 (83.8%) and OpenAI's GPT 5.6 Sol (83.6%) for th...
GLM 5.3 delivered roughly 50% better coding performance than its predecessor and uncovered 2,436 confirmed vulnerabilities across 269 open source projects, including 1,097 critical or high severity bugs.
All benchmark scores are company reported and not yet independently verified. The CyberGym advantage over US rivals is less than one percentage point, and GLM 5.3 remains well behind Mythos 5 and GPT 5.6 Sol on exploi...
What are the key capabilities and benchmark results of Chinese AI firm Zhipu's newly launched GLM-5.3 model, particularly in cybersecurity tZ.ai GLM-5.3 benchmark comparison against Anthropic Mythos 5 and OpenAI GPT-5.6 Sol on CyberGym and ExploitBench.
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Create a landscape editorial hero image for this Studio Global article: What are the key capabilities and benchmark results of Chinese AI firm Zhipu's newly launched GLM-5.3 model, particularly in cybersecurity t. Article summary: On August 14, 2026, Zhipu AI (now branding as **Z.ai**) launched **GLM-5.3**, a 740-billion-parameter open-weight model that delivers roughly 50% better coding performance than GLM-5.2 and unexpectedly strong emergent cy. Topic tags: general, general web, user generated, news. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermarks, charts w
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On August 14, 2026, Zhipu AI — now branding internationally as Z.ai — launched GLM-5.3, a 740-billion-parameter open-weight model that delivers roughly 50% better coding performance than GLM-5.2 and unexpectedly strong emergent cybersecurity capabilities . The model is so capable at finding and exploiting security flaws that Z.ai is delaying the public release of its weights by two weeks for additional safety testing .
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What is the short answer to "Z.ai GLM-5.3: Benchmark Results, Cyber Capabilities, and How It Stacks Up Against GPT-5.6 Sol and Mythos 5"?
On August 14, 2026, Z.ai (formerly Zhipu AI) launched GLM 5.3, a 740 billion parameter open weight model that scores 84.5% on CyberGym — edging past Anthropic's Mythos 5 (83.8%) and OpenAI's GPT 5.6 Sol (83.6%) for th...
What are the key points to validate first?
On August 14, 2026, Z.ai (formerly Zhipu AI) launched GLM 5.3, a 740 billion parameter open weight model that scores 84.5% on CyberGym — edging past Anthropic's Mythos 5 (83.8%) and OpenAI's GPT 5.6 Sol (83.6%) for th... GLM 5.3 delivered roughly 50% better coding performance than its predecessor and uncovered 2,436 confirmed vulnerabilities across 269 open source projects, including 1,097 critical or high severity bugs.
What should I do next in practice?
All benchmark scores are company reported and not yet independently verified. The CyberGym advantage over US rivals is less than one percentage point, and GLM 5.3 remains well behind Mythos 5 and GPT 5.6 Sol on exploi...
Z.ai GLM-5.3: Benchmark Results, Cyber Capabilities, and How It Stacks Up Against GPT-5.6 Sol and Mythos 5
On August 14, 2026, Z.ai (formerly Zhipu AI) launched GLM 5.3, a 740 billion parameter open weight model that scores 84.5% on CyberGym — edging past Anthropic's Mythos 5 (83.8%) and OpenAI's GPT 5.6 Sol (83.6%) for th... GLM 5.3 delivered roughly 50% better coding performance than its predecessor and uncovered 2,436...
Published byEdited with DeepSeek-V4-FlashImages generated with GPT Image 1.5
On August 14, 2026, Z.ai (formerly Zhipu AI) launched GLM 5.3, a 740 billion parameter open weight model that scores 84.5% on CyberGym — edging past Anthropic's Mythos 5 (83.8%) and OpenAI's GPT 5.6 Sol (83.6%) for th...
GLM 5.3 delivered roughly 50% better coding performance than its predecessor and uncovered 2,436 confirmed vulnerabilities across 269 open source projects, including 1,097 critical or high severity bugs.
All benchmark scores are company reported and not yet independently verified. The CyberGym advantage over US rivals is less than one percentage point, and GLM 5.3 remains well behind Mythos 5 and GPT 5.6 Sol on exploi...
What are the key capabilities and benchmark results of Chinese AI firm Zhipu's newly launched GLM-5.3 model, particularly in cybersecurity tZ.ai GLM-5.3 benchmark comparison against Anthropic Mythos 5 and OpenAI GPT-5.6 Sol on CyberGym and ExploitBench.
AI Prompt
Create a landscape editorial hero image for this Studio Global article: What are the key capabilities and benchmark results of Chinese AI firm Zhipu's newly launched GLM-5.3 model, particularly in cybersecurity t. Article summary: On August 14, 2026, Zhipu AI (now branding as **Z.ai**) launched **GLM-5.3**, a 740-billion-parameter open-weight model that delivers roughly 50% better coding performance than GLM-5.2 and unexpectedly strong emergent cy. Topic tags: general, general web, user generated, news. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermarks, charts w
openai.com
On August 14, 2026, Zhipu AI — now branding internationally as Z.ai — launched GLM-5.3, a 740-billion-parameter open-weight model that delivers roughly 50% better coding performance than GLM-5.2 and unexpectedly strong emergent cybersecurity capabilities . The model is so capable at finding and exploiting security flaws that Z.ai is delaying the public release of its weights by two weeks for additional safety testing .
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Continue your research
This page includes a source-backed answer you can continue inside Studio Global.
What is the short answer to "Z.ai GLM-5.3: Benchmark Results, Cyber Capabilities, and How It Stacks Up Against GPT-5.6 Sol and Mythos 5"?
On August 14, 2026, Z.ai (formerly Zhipu AI) launched GLM 5.3, a 740 billion parameter open weight model that scores 84.5% on CyberGym — edging past Anthropic's Mythos 5 (83.8%) and OpenAI's GPT 5.6 Sol (83.6%) for th...
What are the key points to validate first?
On August 14, 2026, Z.ai (formerly Zhipu AI) launched GLM 5.3, a 740 billion parameter open weight model that scores 84.5% on CyberGym — edging past Anthropic's Mythos 5 (83.8%) and OpenAI's GPT 5.6 Sol (83.6%) for th... GLM 5.3 delivered roughly 50% better coding performance than its predecessor and uncovered 2,436 confirmed vulnerabilities across 269 open source projects, including 1,097 critical or high severity bugs.
What should I do next in practice?
All benchmark scores are company reported and not yet independently verified. The CyberGym advantage over US rivals is less than one percentage point, and GLM 5.3 remains well behind Mythos 5 and GPT 5.6 Sol on exploi...
GLM-5.3's headline result is on CyberGym, a benchmark from UC Berkeley's Responsible AI Development Initiative that tests whether a model can identify and validate real vulnerabilities from white-box source code . There, GLM-5.3 scores 84.5%, edging past both Anthropic's Mythos 5 (83.8%) and OpenAI's GPT-5.6 Sol (83.6%).
The picture changes on ExploitBench, which requires deeper reasoning about how a real vulnerability could actually be exploited and demands a working exploit. GLM-5.3 more than doubles its predecessor's score, climbing from 24.4% to 54.4%. However, Z.ai reports that Mythos 5 scores 78% on ExploitBench and GPT-5.6 Sol scores 76.5% — leaving GLM-5.3 well behind on this more difficult measure .
On ExploitGym, which counts how many exploitation tasks a model can complete under a normalized two-hour budget, GLM-5.3 finishes 105 tasks (up from 29 for GLM-5.2) . Within a six-hour budget, that rises to 130 tasks .
Benchmark
What It Measures
GLM-5.3
Anthropic Mythos 5
OpenAI GPT-5.6 Sol
CyberGym
White-box vulnerability discovery & validation from source code
84.5%
83.8%
83.6%
ExploitBench
Root-cause reasoning + delivering a working exploit
54.4%
78%
76.5%
ExploitGym (2-hour budget)
Exploit completions under normalized budget
105 tasks
Not disclosed
Not disclosed
Sources:
Real-World Vulnerability Detection
Beyond benchmarks, GLM-5.3 has been deployed in real-world security testing with impressive results:
2,436 confirmed vulnerabilities found across 269 open-source projects, with results tracked in a public Z.ai Security Disclosure Ledger .
Of those, 1,097 were classified as critical or high-severity.
The model found bugs in software dating back roughly 40 years.
Shortly after launch, it also identified a serious vulnerability in Cursor, the AI-powered code editor .
Key Coding & General Benchmarks
GLM-5.3 also posts strong numbers on coding and agentic benchmarks, positioning it as a leading open-weight model for software engineering tasks:
Terminal-Bench 3.0: scored 28.3 — highest among open-weight models and approaching Anthropic's Claude Fable 5 .
DeepSWE v1.1: jumped from 46.2 to 66.9.
Z.ai internal Code Bench (Max effort): 34.5% (vs. GLM-5.2's 23.4%), using roughly 75,000 output tokens per task versus 96,000 .
Agents' Last Exam: first place among open-weight models .
World of AI Benchmark composite: ranked #6 overall with an 81.3 composite score, just behind Kimi K3 (81.5) and ahead of GPT-5.6 Terra .
FrontierSWE: reached 78.1, up from 67.5 .
SWE-Marathon: doubled from 19.4 to 42.5.
Why Open Weights Are Delayed
Z.ai says the model's cybersecurity capability "grew faster than anticipated" during post-training . The base model's 740 billion parameters were not retrained — all gains came from post-training improvements . Because the capability was an emergent surprise, the company is holding back the open-weight release for two weeks while it strengthens safety and security controls . The model is already available via the Z.ai API and through integrations with ZCode, Claude Code, and OpenCode .
Important Caveats
All GLM-5.3 benchmark scores are company-reported and have not yet been independently verified .
The CyberGym advantage over Mythos 5 and GPT-5.6 Sol is narrow — less than one percentage point .
On ExploitBench, GLM-5.3 trails US rivals by a wide margin (54.4% vs. 78% for Mythos 5 and 76.5% for GPT-5.6 Sol) .
For context, a broader cybersecurity benchmark aggregator lists GPT-5.6 Sol at 84.5% on CyberGym (matching GLM-5.3) and Mythos 5 at 83.8%, while showing an ExploitBench of 73.5% for Sol and 78% for Mythos 5 .
x.com
Preparing GLM-5.3 for Open Release: A Responsible Path to Cyber ...
GLM-5.3's headline result is on CyberGym, a benchmark from UC Berkeley's Responsible AI Development Initiative that tests whether a model can identify and validate real vulnerabilities from white-box source code . There, GLM-5.3 scores 84.5%, edging past both Anthropic's Mythos 5 (83.8%) and OpenAI's GPT-5.6 Sol (83.6%).
The picture changes on ExploitBench, which requires deeper reasoning about how a real vulnerability could actually be exploited and demands a working exploit. GLM-5.3 more than doubles its predecessor's score, climbing from 24.4% to 54.4%. However, Z.ai reports that Mythos 5 scores 78% on ExploitBench and GPT-5.6 Sol scores 76.5% — leaving GLM-5.3 well behind on this more difficult measure .
On ExploitGym, which counts how many exploitation tasks a model can complete under a normalized two-hour budget, GLM-5.3 finishes 105 tasks (up from 29 for GLM-5.2) . Within a six-hour budget, that rises to 130 tasks .
Benchmark
What It Measures
GLM-5.3
Anthropic Mythos 5
OpenAI GPT-5.6 Sol
CyberGym
White-box vulnerability discovery & validation from source code
84.5%
83.8%
83.6%
ExploitBench
Root-cause reasoning + delivering a working exploit
54.4%
78%
76.5%
ExploitGym (2-hour budget)
Exploit completions under normalized budget
105 tasks
Not disclosed
Not disclosed
Sources:
Real-World Vulnerability Detection
Beyond benchmarks, GLM-5.3 has been deployed in real-world security testing with impressive results:
2,436 confirmed vulnerabilities found across 269 open-source projects, with results tracked in a public Z.ai Security Disclosure Ledger .
Of those, 1,097 were classified as critical or high-severity.
The model found bugs in software dating back roughly 40 years.
Shortly after launch, it also identified a serious vulnerability in Cursor, the AI-powered code editor .
Key Coding & General Benchmarks
GLM-5.3 also posts strong numbers on coding and agentic benchmarks, positioning it as a leading open-weight model for software engineering tasks:
Terminal-Bench 3.0: scored 28.3 — highest among open-weight models and approaching Anthropic's Claude Fable 5 .
DeepSWE v1.1: jumped from 46.2 to 66.9.
Z.ai internal Code Bench (Max effort): 34.5% (vs. GLM-5.2's 23.4%), using roughly 75,000 output tokens per task versus 96,000 .
Agents' Last Exam: first place among open-weight models .
World of AI Benchmark composite: ranked #6 overall with an 81.3 composite score, just behind Kimi K3 (81.5) and ahead of GPT-5.6 Terra .
FrontierSWE: reached 78.1, up from 67.5 .
SWE-Marathon: doubled from 19.4 to 42.5.
Why Open Weights Are Delayed
Z.ai says the model's cybersecurity capability "grew faster than anticipated" during post-training . The base model's 740 billion parameters were not retrained — all gains came from post-training improvements . Because the capability was an emergent surprise, the company is holding back the open-weight release for two weeks while it strengthens safety and security controls . The model is already available via the Z.ai API and through integrations with ZCode, Claude Code, and OpenCode .
Important Caveats
All GLM-5.3 benchmark scores are company-reported and have not yet been independently verified .
The CyberGym advantage over Mythos 5 and GPT-5.6 Sol is narrow — less than one percentage point .
On ExploitBench, GLM-5.3 trails US rivals by a wide margin (54.4% vs. 78% for Mythos 5 and 76.5% for GPT-5.6 Sol) .
For context, a broader cybersecurity benchmark aggregator lists GPT-5.6 Sol at 84.5% on CyberGym (matching GLM-5.3) and Mythos 5 at 83.8%, while showing an ExploitBench of 73.5% for Sol and 78% for Mythos 5 .
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