GLM-5.3 来了：编程能力开源第一，还顺手揪出潜伏 40 年的世界级漏洞
Ranking
Overall
71
Content
80
Popularity
N/A
No observed public metrics; popularity remains neutral/archived.
Merged summary
TL;DR - Zhipu launched GLM-5.3, an open model emphasizing coding agents and cybersecurity, with reported gains driven primarily by post-training rather than a new base model. It also powers ZCode and AutoClaw and is available through GLM Coding Plan.
- GLM-5.3 reportedly scores 66.9 on DeepSWE v1.1 and 28.3 on Terminal-Bench 3.0, targeting long-horizon software engineering and tool use.
- IndexShare shares sparse-attention indexers across Transformer layers, reportedly reducing per-token FLOPs by 2.9× at a 1M-token context length.
- The asynchronous SAO reinforcement-learning method supports trajectories exceeding 1,000 steps, while the Slime framework separates rollout and training to shorten post-training cycles.
- Zhipu says GLM models helped identify 2,436 vulnerabilities across 269 projects, including 1,097 medium- or high-severity issues and a decades-old DNS amplification risk.
Sources (1)
GLM-5.3 来了：编程能力开源第一，还顺手揪出潜伏 40 年的世界级漏洞
Public signals
N/A
TL;DR - Zhipu launched GLM-5.3, an open model emphasizing coding agents and cybersecurity, with reported gains driven primarily by post-training rather than a new base model. It also powers ZCode and AutoClaw and is available through GLM Coding Plan.
- GLM-5.3 reportedly scores 66.9 on DeepSWE v1.1 and 28.3 on Terminal-Bench 3.0, targeting long-horizon software engineering and tool use.
- IndexShare shares sparse-attention indexers across Transformer layers, reportedly reducing per-token FLOPs by 2.9× at a 1M-token context length.
- The asynchronous SAO reinforcement-learning method supports trajectories exceeding 1,000 steps, while the Slime framework separates rollout and training to shorten post-training cycles.
- Zhipu says GLM models helped identify 2,436 vulnerabilities across 269 projects, including 1,097 medium- or high-severity issues and a decades-old DNS amplification risk.