GLM-5.2
GLM-5.2 delivers advanced capabilities for extended task sequences and complex projects.
Open SourceCoding & Dev
Visit website Free to browse · updated 2026-06-19
What is GLM-5.2?
GLM-5.2 represents an evolution in handling prolonged operations by maintaining coherence over very large input sequences. The system emphasizes practical reliability during extended agent-driven activities such as software development and optimization processes. Key enhancements include mechanisms to share computational resources across layers, which lowers overall processing demands while preserving output quality. Additional refinements to decoding methods further improve speed without sacrificing accuracy in multi-step generations. Released under an open license, the model stands out among similar tools for its balance of accessibility and specialized performance in technical domains. Users can select different effort modes to prioritize either rapid responses or deeper analysis depending on the situation.
Key features
Solid 1M-token context
Advanced coding with multiple thinking effort levels
IndexShare architecture reducing per-token FLOPs by 2.9× at 1M context
Improved MTP layer increasing acceptance length by up to 20%
MIT open-source license
Strong performance on FrontierSWE, PostTrainBench and SWE-Marathon
Effort level control for balancing capability and latency
AI models GLM-5.2 uses
GLM-5.2
latest flagship model for long-horizon tasks with solid 1M context and effort level control
What you can use GLM-5.2 for
Long-Horizon Coding Projects
Handle extended engineering workflows such as large-scale code construction, systems optimization, and complex debugging using the stable 1M-token context for sustained agentic tasks.
Flexible Performance Tuning
Adjust thinking effort levels to balance coding capability against latency and compute cost during agentic development and post-training optimization scenarios.
Open-Source Model Integration
Deploy the MIT-licensed model in research or production environments without regional restrictions, leveraging architecture improvements for efficient long-context inference.
How to use GLM-5.2
- 1Visit Z.ai to access the model
- 2Choose API, GitHub, or HuggingFace download
- 3Select desired effort level for the task
- 4Provide coding prompt or agent trajectory
- 5Review output and iterate as needed
GLM-5.2 pricing
Pricing model: Open Source. Plan details are indicative — check the site for current prices.
MIT License
Free
- MIT open-source license
- No regional limits
- Technical access without borders
- GitHub and HuggingFace availability
Editor's verdict
Pros
- +Highest-ranked open-source model on long-horizon coding benchmarks
- +Practical sustained engineering capability at 1M context
Cons
- –Trails Opus 4.8 by 13% on SWE-Marathon
- –Still has room to grow on ultra-long-horizon tasks
Our take: GLM-5.2 is a solid coding & dev choice. It's valued for highest-ranked open-source model on long-horizon coding benchmarks and practical sustained engineering capability at 1m context. The main trade-off is trails opus 4.8 by 13% on swe-marathon. A good pick if you want capable AI without a high upfront cost.
Frequently asked questions
It provides a solid 1M-token context that maintains quality across long coding-agent trajectories and engineering workloads.
Summary
GLM-5.2 is a solid coding & dev choice. It's valued for highest-ranked open-source model on long-horizon coding benchmarks and practical sustained engineering capability at 1m context. The main trade-off is trails opus 4.8 by 13% on swe-marathon. A good pick if you want capable AI without a high upfront cost.
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