GLM-5 is a Mixture-of-experts (MOE) model that has:<\/p>\n\n\n\n
- \n
- 744B total parameters<\/strong> with 40B used at once, compared to 355B total and 32B active in GLM-4.5.<\/li>\n\n\n\n
- Pre-training data:<\/strong> Increased to 28.5T tokens (23T).<\/li>\n\n\n\n
- Context window:<\/strong> 200K\u2013205K tokens, with strong long-context retention.<\/li>\n\n\n\n
- Max output:<\/strong> Up to 128K tokens.<\/li>\n\n\n\n
- DeepSeek Sparse Attention (DSA):<\/strong> It lowers the cost to run the model while still working well with long inputs.<\/li>\n<\/ul>\n\n\n\n
The team created a new training system called SLIME. It helps improve the model faster after training.<\/p>\n\n\n\n
This training helps the model turn its basic skills into strong performance for AI agent tasks.<\/p>\n\n\n\n
The full model uses BF16 precision and is very large (about 1.5TB to download). Smaller compressed versions are also available and need much less space.<\/p>\n\n\n\n
It uses the MIT license and is fully open-source on Hugging Face and ModelScope. You can run it on platforms like Ollama, Together AI, and NVIDIA NIM.<\/p>\n\n\n\n
The model was trained and run on Chinese hardware, such as Huawei Ascend chips and the MindSpore framework. This helps reduce the need for foreign technology.<\/p>\n\n\n\n
Performance Highlights and Benchmarks of GLM-5<\/h2>\n\n\n\n
![\"glm](\"https:\/\/cdnblog.webkul.com\/blog\/wp-content\/uploads\/2026\/03\/glm5-1200x816.webp\")
<\/figure>\n\n\n\nGLM-5 performs better (among open-weights models) in reasoning, coding and agentic tasks. <\/p>\n\n\n\n
It performs much better than GLM-4.7 and is close to top closed-source models like Claude Opus from Anthropic.<\/p>\n\n\n\n
Key reported scores :<\/p>\n\n\n\n
- \n
- The Last Exam of Humanity (HLE)<\/a>:<\/strong> 50.4% (using tools; +7.6 percent better in particular evals).<\/li>\n\n\n\n
- SWE-bench Verified: <\/strong>~77.8% (good coding\/systems engineering performance, nearly best closed models).<\/li>\n\n\n\n
- GPQA-Diamond:<\/strong> 86.0%.<\/li>\n\n\n\n
- AIME 2026<\/strong>: 92.7%.<\/li>\n\n\n\n
- BrowseComp:<\/strong> 75.9% (+8.4%).<\/li>\n\n\n\n
- Terminal-Bench-2.0:<\/strong> 61.1% (+28.3%).<\/li>\n\n\n\n
- Good performances on MATH, MMLU-Pro and IFEval and on internal suites such as CC-Bench-V2.<\/li>\n<\/ul>\n\n\n\n
It ranks very high among open-source models for agent tasks and long problems. It also reduces false answers by up to 56% compared to GLM-4.7.<\/p>\n\n\n\n
In Artificial Analysis Intelligence Index, it also scored the first open-weights position (score of about 50, which was 42 in GLM-4.7).<\/p>\n\n\n\n
Early users praise its coding skills and reliability for AI agents<\/a>.<\/p>\n\n\n\n
Some say it can be slower or give longer answers than some closed models.<\/p>\n\n\n\n
Competitor Analysis<\/h2>\n\n\n\n
1. Vs. GLM-5 and Sonnet variants <\/strong>are much cheaper open models and perform well on coding and agent tests like SWE-bench.<\/p>\n\n\n\n
In API systems, some analyses refer to it as having been Opus 20 percent of the price.<\/p>\n\n\n\n
2. Vs. GPT-4o:<\/strong> Greater context, better on recent agentic\/reasoning evals, and less costly in most instances. GPT-4o is more ancient (2024) and has a smaller context (128K).<\/p>\n\n\n\n
3. Vs. other open models<\/strong> (DeepSeek V3, Qwen series, etc.): GLM-5 sets a new high bar for scale + agentic focus among openly available weights.<\/p>\n\n\n\n
The API price is competitive, about $1 per million input tokens and $3.20 per million output tokens, which is cheaper than many top closed models.<\/p>\n\n\n\n
Soon after, a faster and cheaper version called GLM-5-Turbo was released for agent workflows. It is not fully open-source.<\/p>\n\n\n\n
Why Agentic Engineering Matters<\/h2>\n\n\n\n
The philosophy shift is depicted in the tagline: From Vibe Coding to Agentic Engineering.<\/p>\n\n\n\n
The previous models were good at quick and creative vibe-based coding. GLM-5 focuses on long-standing and reliable agents which can:<\/p>\n\n\n\n
- \n
- Soft engineering Multi-file.<\/li>\n\n\n\n
- The use of tools and long-chain performance.<\/li>\n\n\n\n
- Filthy systems planning and debugging.<\/li>\n\n\n\n
- OpenClaw persistent automation integrations.<\/li>\n<\/ul>\n\n\n\n
This follows the industry trend of building AI agents that can design and manage large systems, not just write code.<\/p>\n\n\n\n
Availability and How to Try It<\/h2>\n\n\n\n
- \n
- Hugging Face:<\/strong> zai-org\/GLM-5 (weights + code).<\/li>\n\n\n\n
- Ollama:<\/strong> run glm-5\/ollama run glm-5.<\/li>\n\n\n\n
- API: <\/strong>Using Z.human, Together AI, OpenRouter and more.<\/li>\n\n\n\n
- Local programs:<\/strong> Unsloth, llama.cpp, or SGLang to run program inference. The full model needs strong hardware and about 1.65TB of disk space. Smaller versions can run on lighter systems.<\/li>\n<\/ul>\n\n\n\n
Final Thoughts<\/h2>\n\n\n\n
GLM-5 is a milestone that is striking, not only to Z.ain or the domestic AI ecosystem of China, but to the entire open-source community in the world.<\/p>\n\n\n\n
It offers strong AI agent abilities with an MIT license. It also uses new ideas like DSA and advanced RL to work more efficiently.<\/p>\n\n\n\n
This makes powerful Artifical Intelligence<\/a> tools easier for more people to use, without paying for costly APIs.<\/p>\n\n\n\n
GLM-5 is worth trying, whether you are building agents, or working with large codebases, or even with local LLM.<\/p>\n\n\n\n
It is an indication that open-weights models are closing the gap with closed giants at an unbelievable rate.<\/p>\n\n\n\n
The competition of more competent, effective, and independent AI increases.<\/p>\n\n\n\n
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Want to Build AI-powered solutions visit Webkul<\/a>!<\/em><\/p>\n<\/blockquote>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
- Ollama:<\/strong> run glm-5\/ollama run glm-5.<\/li>\n\n\n\n
- Hugging Face:<\/strong> zai-org\/GLM-5 (weights + code).<\/li>\n\n\n\n
- SWE-bench Verified: <\/strong>~77.8% (good coding\/systems engineering performance, nearly best closed models).<\/li>\n\n\n\n
- Pre-training data:<\/strong> Increased to 28.5T tokens (23T).<\/li>\n\n\n\n