GLM-5.2
A flagship model from Zhipu AI made available to all GLM Coding Plan users. It supports extremely long context at the EM level, offers two reasoning intensity modes designated HIGH and MAX, and has an open-source version licensed under MIT.
Core Facts
Timeline (last 90 days)
Reflection claims Beam's reasoning ability is comparable to GLM-5.2, but its reasoning compute is about 1/3 to 1/4 of the latter's.
Hugging Face在2026年7月遭遇自主AI代理入侵，安全事件后其试图用商业模型调查但被提供商的安全措施拒绝，最终在其自有基础设施上运行开放权重模型GLM-5.2完成日志分析。
GLM-5.3-Flash 在 DeepSWE v1.1 得63.4分，比 GLM-5.2 的46.2分提升17.2分；HLE 得55.3分。
在 Surge AI 组织的盲评中，专业标注员给 Mistral Large 4 预览版打出 3.74/5，在五个模型中排名第二，落后于 Claude Opus 5（4.22），领先于 GLM-5.3、Kimi K3 和 GLM-5.2。
During a Hugging Face security incident, the Claude Opus and Fable models rejected the majority of vulnerability investigation work due to their guardrail mechanisms, and the team ultimately switched to GLM-5.2 running on their own hardware to continue making progress.
Among model choices, GLM-5.2 is the second-best option with stable overall performance, HY3 is suited for simple webpages, and the Auto default mode is not recommended because it may randomly select models and cause unstable output.
Earlier models Claude Opus 4.6 and GLM-5.2 had a success rate of zero on these exploitation tasks.
The team performed one epoch of fine-tuning on 477,642 synthetic reasoning trajectories distilled from GLM-5.2, covering 22,000 curated problems spanning 16 competitive programming contest systems.
GLM-5.2 was chosen as the teacher model due to higher accuracy and a roughly 30% reduction in generation length.
The team compared two SFT teacher model approaches based on DeepSeek-V4-Flash and GLM-5.2 respectively, ultimately selecting GLM-5.2.
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All Facts (20)
Zhipu released GLM-5.3, claiming its coding capability improved by 50% compared to GLM-5.2.
65%UnverifiedReflection claims Beam's reasoning ability is comparable to GLM-5.2, but its reasoning compute is about 1/3 to 1/4 of the latter's.
50%UnverifiedGLM-5.3-Flash 在 DeepSWE v1.1 得63.4分，比 GLM-5.2 的46.2分提升17.2分；HLE 得55.3分。
50%Unverified在 Surge AI 组织的盲评中，专业标注员给 Mistral Large 4 预览版打出 3.74/5，在五个模型中排名第二，落后于 Claude Opus 5（4.22），领先于 GLM-5.3、Kimi K3 和 GLM-5.2。
50%UnverifiedAmong model choices, GLM-5.2 is the second-best option with stable overall performance, HY3 is suited for simple webpages, and the Auto default mode is not recommended because it may randomly select models and cause unstable output.
50%UnverifiedEarlier models Claude Opus 4.6 and GLM-5.2 had a success rate of zero on these exploitation tasks.
50%UnverifiedGLM-5.2 was chosen as the teacher model due to higher accuracy and a roughly 30% reduction in generation length.
50%UnverifiedThe team performed one epoch of fine-tuning on 477,642 synthetic reasoning trajectories distilled from GLM-5.2, covering 22,000 curated problems spanning 16 competitive programming contest systems.
50%UnverifiedThe team compared two SFT teacher model approaches based on DeepSeek-V4-Flash and GLM-5.2 respectively, ultimately selecting GLM-5.2.
50%UnverifiedZhipu did not retrain the model base, but retained GLM-5.2's base and only expanded and reinforced it during the post-training phase.
50%UnverifiedStatements in official announcements such as 'strongest' and 'continuous lead' all come from Zhipu itself and still require corroboration from independent third-party evaluations.
50%UnverifiedGLM-5.2 maintains a continuous lead in the ability to independently complete long-horizon tasks.
50%UnverifiedGLM-5.2 continues its positioning as the core engine for Zhipu's ambition to build the strongest domestically produced coding model.
50%UnverifiedThe ModelKey for GLM-5.2 is GLM-5.2.
50%UnverifiedGLM-5.2 offers an ETA (Estimated Time of Arrival) design that gives users a predictable sense of control.
50%UnverifiedEven the most advanced current models such as GLM-5.2, Claude-Sonnet-4.6, and GPT achieve only just over half marks on this benchmark.
50%UnverifiedThe research team conducted extensive evaluations using multiple agent frameworks on both proprietary models and open-weight models; state-of-the-art models such as GLM-5.2 and Claude-Sonnet-4.6 achieved only slightly above passing performance.
50%UnverifiedState-of-the-art models such as GLM-5.2, Claude-Sonnet-4.6, and GPT-5 achieve only approximately 50–54% Pass@8 on the hard subset.
50%UnverifiedGLM-5.3-Flash has improvements over the previous generation GLM-5.2 in coding capability, robustness of agent interaction, and visual understanding.
50%UnverifiedGLM-5.2 首次将长程任务能力建立在稳定可用的 100 万 token 上下文之上
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