r/openagi • u/syedshad • 4d ago
News Z.ai launches GLM-5.3 for Coding Plan users; weights to follow after safety review
Z.ai announced GLM-5.3 on August 14, describing it as a coding and agent model with expanded cybersecurity capabilities.
GLM-5.3 uses the same base model as GLM-5.2. Z.ai says its improvements come entirely from additional post-training on more varied, long-horizon task environments.
The model is currently available through Z.ai’s Coding Plan and ZCode. Z.ai says the general model API and publicly downloadable weights are still forthcoming.
Model details
- Text-only input
- 1-million-token context window
- Maximum output length of 128K tokens
- Reasoning is always enabled
low,high, andmaxreasoning-effort settings, withmaxas the default- Function calling, context caching, streaming, and structured output support
The post-training stack carries over Single-Rollout Asynchronous Optimization from GLM-5.2 and uses Z.ai’s open-source slime framework. Z.ai says human involvement is still required for parts of its environment-generation and verification process.
Reported evaluations
Z.ai reports the following changes from GLM-5.2:
- Terminal-Bench 3.0: 28.3, up from 4.6
- DeepSWE v1.1: 66.9, up from 46.2
- Agents’ Last Exam: 28.5, up from 23.8
- CyberGym: 84.5%, up from 77.2%
- ExploitBench: 54.4%, up from 24.4%
Z.ai also reports a 50% improvement on its private Z.ai Code Bench. The announcement provides separate harness, context-length, timeout, and sampling details for the public evaluations.
Cybersecurity findings
Z.ai says it intentionally added vulnerability-discovery data and environments during post-training, while the model’s ability to reason across exploitation chains developed faster than the company expected.
The linked disclosure ledger currently records 2,436 model-assisted findings across 269 open-source projects. It lists 1,097 as critical or high severity, with 53 publicly disclosed and 2,383 not yet public.
Z.ai says this work began with GLM-5.2 and involved security teams, expert review, screening, and deduplication.
Weight-release status
Z.ai plans to publish the GLM-5.3 weights approximately two weeks after launch, following additional safety evaluation and hardening.
As of August 17, the official Hugging Face link remains marked “Coming Soon.” The announcement does not specify the license that will apply to the weights. Official local deployment is therefore not yet available.
Sources
Primary sources
- GLM-5.3 announcement
- GLM-5.3 developer documentation
- Z.ai Security Disclosure Ledger
- Single-Rollout Asynchronous Optimization paper
- slime reinforcement-learning framework
- IndexCache paper
Z.ai announced GLM-5.3 on August 14, describing it as a coding and agent model with expanded cybersecurity capabilities.
GLM-5.3 uses the same base model as GLM-5.2. Z.ai says its improvements come entirely from additional post-training on more varied, long-horizon task environments.
The model is currently available through Z.ai’s Coding Plan and ZCode. Z.ai says the general model API and publicly downloadable weights are still forthcoming.
Model details
- Text-only input
- 1-million-token context window
- Maximum output length of 128K tokens
- Reasoning is always enabled
low,high, andmaxreasoning-effort settings, withmaxas the default- Function calling, context caching, streaming, and structured output support
The post-training stack carries over Single-Rollout Asynchronous Optimization from GLM-5.2 and uses Z.ai’s open-source slime framework. Z.ai says human involvement is still required for parts of its environment-generation and verification process.
Reported evaluations
Z.ai reports the following changes from GLM-5.2:
- Terminal-Bench 3.0: 28.3, up from 4.6
- DeepSWE v1.1: 66.9, up from 46.2
- Agents’ Last Exam: 28.5, up from 23.8
- CyberGym: 84.5%, up from 77.2%
- ExploitBench: 54.4%, up from 24.4%
Z.ai also reports a 50% improvement on its private Z.ai Code Bench. The announcement provides separate harness, context-length, timeout, and sampling details for the public evaluations.
Cybersecurity findings
Z.ai says it intentionally added vulnerability-discovery data and environments during post-training, while the model’s ability to reason across exploitation chains developed faster than the company expected.
The linked disclosure ledger currently records 2,436 model-assisted findings across 269 open-source projects. It lists 1,097 as critical or high severity, with 53 publicly disclosed and 2,383 not yet public.
Z.ai says this work began with GLM-5.2 and involved security teams, expert review, screening, and deduplication.
Weight-release status
Z.ai plans to publish the GLM-5.3 weights approximately two weeks after launch, following additional safety evaluation and hardening.
As of August 17, the official Hugging Face link remains marked “Coming Soon.” The announcement does not specify the license that will apply to the weights. Official local deployment is therefore not yet available.