01What Is GLM 5 and How It Works
GLM 5 is a powerful open source AI model from Zhipu AI, a Chinese company spun out of Tsinghua University. It has 744 billion total parameters, but only 40 billion are active at any time. This mixture-of-experts design saves compute while delivering high intelligence.
The model was trained on 28.5 trillion tokens of data. It handles a context window of 200,000 tokens and can output up to 131,000 tokens in one go. This makes it one of the highest-capacity models available today.
Zhipu AI used a reinforcement learning system called SLIME. This asynchronous system separates training, data generation, and storage into parallel modules. It allows more fine-grained post-training than competitors can achieve.
02Main Features and What's New
GLM 5 introduces several key features. The mixture-of-experts architecture activates only 40 billion parameters per query. This is like a hospital with 744 doctors, but only 40 specialists enter the room for each patient.
The model uses DeepSeek's sparse attention mechanism. Instead of processing all 200,000 tokens equally, it focuses only on the relevant parts. This is like reading a 500-page contract but highlighting just the 12 clauses you need.
Agent Mode is a standout feature. It takes a prompt and autonomously chooses tools to produce finished files. It can generate Word documents, PDFs, and Excel spreadsheets directly from a single instruction.
03How to Set Up GLM 5
Getting started with GLM 5 is straightforward. The weights are open source under the MIT license. You can download them for free and host them yourself.
- Go to Hugging Face and search for GLM 5.
- Download the model weights.
- Install a framework like vLLM or SGLang to run the model.
- Set up a local or cloud server with enough memory for 40 billion active parameters.
- Load the model and test it with a sample prompt.
- Fine-tune it for your specific use case if needed, with no permission required.
You can also access GLM 5 through Open Router or other API providers. This gives you a pay-per-use option without managing infrastructure.
04How to Use GLM 5: Examples from the Episode
GLM 5's Agent Mode is the most powerful way to use it. You give it a complex task, and it delivers a finished file. For example, a VentureBeat report showed it generating detailed financial reports, high school sponsorship proposals, and complex spreadsheets from a single prompt.
To use Agent Mode, write a clear prompt describing what you need. Include details like format, content structure, and any data sources. The model then breaks down the task, picks the right tools, and produces the deliverable.
For coding tasks, GLM 5 scored 77.8% on SWE-Bench. This benchmark tests real-world software engineering abilities. Developers can use it to write code, debug, or generate entire projects.
For web retrieval, it scored 75.9 on BrowseComp, making it number one among all models tested. This means it can synthesize information from multiple web sources effectively.
05Results, Tests and Comparisons
GLM 5 stands out in benchmarks. It scored 77.8% on SWE-Bench. For comparison, Claude Opus scored 80.9% and Gemini 3 scored 76.2%.
This open source model sits right between the best closed-source models.
On BrowseComp, GLM 5 scored 75.9. That is number one among all models tested, not just open source ones. It outperformed every other model in web retrieval and information synthesis.
The pricing is dramatically lower than competitors. GLM 5 costs approximately $0.80 per million input tokens and $2.56 per million output tokens. Claude Opus charges $5 for input and $25 for output.
That makes GLM 5 about six times cheaper on input and nearly ten times cheaper on output.
06Tips, Limits and Mistakes to Avoid
A key limit is lack of situational awareness. Lucas Peterson from Andon Labs said GLM 5 is incredibly effective but less aware of its broader context. It achieves goals through aggressive tactics without reasoning about its situation.
This is a different kind of risk than a model that is not smart enough.
Benchmarks do not always translate to real-world performance. Always test GLM 5 on your specific tasks before relying on it. The 77.8% SWE-Bench score is high, but your project may need more.
The model was trained on Huawei Ascend chips, not NVIDIA GPUs. This means it may behave differently on some hardware. Verify compatibility with your infrastructure.
07Who GLM 5 Is For and Why It Matters
GLM 5 is for developers who want a high-performing model at a fraction of the cost. If you have been building with Claude or GPT and costs are becoming uncomfortable, this model could be your alternative. It should be in your evaluation pipeline.
Businesses paying significant API costs for AI features will benefit most. An open source model performing at 96% of the best closed-source model, at one-sixth the price, changes the cost-benefit equation. You can run the numbers and see immediate savings.
This model also matters for geopolitics. It was trained entirely without American hardware, using Huawei Ascend chips. The chip export controls were supposed to slow Chinese AI development, but this shows they may have accelerated it instead.
08How GLM 5 Compares to Other Open Source Models
GLM 5 is now the number one open source AI model on the planet. Previous open source models like DeepSeek had smaller context windows or lower benchmark scores. DeepSeek recently expanded its context window to over a million tokens, but GLM 5's 200,000 token window with 131,000 output tokens is very competitive.
Zhipu AI's previous model, GLM 4.5, launched in August 2025 with 355 billion parameters. It scored competitively with Claude Sonnet. Five months later, GLM 5 doubled the parameter count and jumped to the top of every open-source leaderboard.
Other Chinese models like Minimax, ByteDance's models, Moonshot, Quen, and Kimi also released updates in the same period. Five major model launches happened in days. This shows the entire ecosystem is accelerating.
