Architecture Base and Post-Training Stack
Zhipu AI has officially released its next-generation flagship model, GLM-5.3. Retaining the 743B-parameter base from its predecessor, all performance gains stem from scaled post-training reinforcement learning.
Regarding its underlying infrastructure, GLM-5.3 leverages the IndexShare long-context engine, the SAO reinforcement learning algorithm designed for long-horizon tasks, and the open-source Slime asynchronous distributed training framework to collect signals across thousands of diverse interactive environments, driving a leap in long-sequence reasoning.
Coding and Cybersecurity Benchmark Performance
Official evaluation data demonstrates substantial progress on long-horizon terminal execution, software engineering repair, and vulnerability discovery. Notably in cybersecurity evaluations, the model exhibited emergent multi-step reconnaissance and exploit-chain reasoning capabilities.
Core Benchmark Performance
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28.3
Terminal Bench 3.0 score, marking a major leap from 4.6 in the previous version.
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84.5%
CyberGym real-world vulnerability benchmark accuracy, leading current evaluation tables.
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66.9
DeepSWE v1.1 software engineering benchmark score, up from 46.2 previously.
Across benchmark comparisons with frontier models, GLM-5.3 exhibits comprehensive gains across 16 benchmarks spanning Coding, Cyber, and Agentic domains, establishing leading comprehensive engineering capability among open models:
| Benchmark Category and Metric | GLM-5.3 | GLM-5.2 | Kimi K3 | DeepSeek-V4 Pro-0813 | Qwen3.8-Max | Opus 4.8 | Fable 5 (w/ fallback) | GPT-5.6 Sol |
|---|---|---|---|---|---|---|---|---|
| Terminal Bench 2.1 | 88.2 | 81.0 | 88.3 | 87.9 | 86.6 | 85.0 | 88.0 | 88.8 |
| Terminal Bench 3.0 | 28.3 | 4.6 | 17.4 | - | - | 21.1 | 33.7 | 34.6 |
| DeepSWE v1.1 | 66.9 | 46.2 | 67.5 | 62.7 | 56.6 | 58.0 | 69.7 | 72.7 |
| NL2Repo | 58.0 | 48.9 | 58.0 | 61.1 | 55.9 | 69.7 | - | - |
| ProgramBench Almost Solved | 19.0 | 9.5 | 17.5 | - | 10.5 | 15.5 | 33.0 | 23.0 |
| FrontierSWE | 78.1 | 67.5 | - | - | - | 66.5 | 88.2 | - |
| SWE-Marathon v1.1 | 42.5 | 19.4 | 48.1 | - | - | 48.8 | 33.1 | 42.5 |
| PostTrainBench | 39.8 | 31.7 | 32.0 | - | - | 32.9 | 41.8 | 36.2 |
| CyberGym | 84.5 | 77.2 | 80.0 | 83.3 | 78.5 | 78.1 | 83.8 | 83.6 |
| ExploitGym 2h / 6h | 105 / 130 | 29 / 39 | 36 / 70 | - | 14 / 26 | 80 / 120 | 181 / 247 | 216 / 293 |
| ExploitBench | 54.4 | 24.4 | 32.2 | - | 28.8 | 40.0 | 78.0 | 76.5 |
| Toolathlon Verified | 73.0 | 59.9 | 76.5 | 74.1 | 72.5 | 76.2 | 74.7 | 74.9 |
| AutomationBench v1.0.6 | 48.2 | 26.2 | 46.7 | 43.2 | 39.8 | 41.0 | 46.2 | 45.8 |
| Agents' Last Exam (ALE-CLI) | 28.5 | 23.8 | 27.6 | 25.7 | 27.0 | 25.7 | 23.8 | 28.6 |
| HLE w/ Tools | 62.5 | 54.7 | 59.8 | 60.0 | 56.2 | 57.9 | 63.9 | 64.5 |
| GDPval-AA v2 | 1769 | 1508 | 1682 | 1590 | 1739 | 1588 | 1743 | 1730 |
Across specific category evaluations, GLM-5.3 ranks first across major benchmarks including CyberGym vulnerability discovery (84.5%), AutomationBench workflow execution (48.2%), and the long-horizon benchmark GDPval-AA v2 (1769), surpassing contemporary proprietary and open-source models.
Mandatory Reasoning and Effort Levels
Regarding API interactions, GLM-5.3 alters its reasoning paradigm by deprecating direct output and enforcing mandatory thinking reasoning. Developers can configure computational budgets per task using explicit effort parameters.
The system provides three reasoning effort tiers: low for lightweight formatting and simple queries, high for balanced speed and token efficiency, and max as the default setting for intricate algorithmic design and multi-step debugging.
Availability and Open-Source Roadmap
GLM-5.3 is currently available through the official API and dedicated coding subscription plans. Addressing open-source community expectations, the team confirmed plans to open-source model weights within two weeks following safety reviews and alignment hardening.