GLM-5
GLM-5 is Zhipu AI's 744B parameter open-weight powerhouse, excelling in long-horizon agentic tasks, coding, and factual accuracy with a 200k context window.
zhipuGLMFebruary 11, 2026About GLM-5
Learn about GLM-5's capabilities, features, and how it can help you achieve better results.
GLM-5 is Zhipu AI's flagship foundation model designed for autonomous agentic workflows and complex systems engineering. It utilizes a massive 744 billion parameter Mixture-of-Experts (MoE) architecture, with 40 billion parameters active during inference to balance performance and speed. The model is the first open-weight system to demonstrate parity with proprietary frontier models in software engineering tasks, scoring 77.8% on SWE-bench Verified.
The model was trained on 28.5 trillion tokens using a domestic cluster of 100,000 Huawei Ascend chips. It integrates specialized mechanisms like Multi-head Latent Attention (MLA) and DeepSeek Sparse Attention (DSA) to maintain logical consistency across its 200,000 token context window. This technical stack allows GLM-5 to handle long-horizon planning and resource management without the high latency typical of dense models of this size.
Zhipu AI released GLM-5 under the MIT license, enabling enterprise users to deploy the weights locally for sensitive data processing. With an input cost of just $1.00 per million tokens, it offers a 6x price advantage over rival models like Claude 4.5. The model includes a dedicated Thinking Mode that reduces hallucination rates significantly compared to its predecessors.
Use Cases
Discover the different ways you can use GLM-5 to achieve great results.
Autonomous Software Engineering
Solving complex GitHub issues and performing repo-wide refactors by utilizing its 77.8% score on SWE-bench Verified.
Enterprise Tool Orchestration
Executing multi-step agentic workflows across internal APIs to handle back-office automation in finance and legal sectors.
Long-Context Repository Analysis
Using the 200,000 token window to ingest and analyze entire documentation sets or multi-file codebases in a single pass.
Personal AI Coworkers
Powering open-source agents like OpenClaw to manage emails, calendars, and background tasks 24/7 with high reliability.
On-Premise Private Intelligence
Deploying the open-weight model locally under its MIT license to ensure full data privacy for sensitive corporate operations.
Cost-Efficient Agent Scaling
Running high-volume agentic sessions at 6-8x lower costs compared to proprietary frontier models without sacrificing reasoning depth.
Strengths
Limitations
API Quick Start
zai/glm-5
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.ZHIPU_API_KEY,
baseURL: "https://open.bigmodel.cn/api/paas/v4/",
});
const response = await client.chat.completions.create({
model: "glm-5",
messages: [{ role: "user", content: "Analyze this repo structure and refactor to GraphQL." }],
stream: true,
});
for await (const chunk of response) {
process.stdout.write(chunk.choices[0]?.delta?.content || "");
}Install the SDK and start making API calls in minutes.
Community Feedback
See what the community thinks about GLM-5
“GLM-5 is an open-source 744B parameter model that performs near Claude Opus level on coding... but the price difference matters.”
“The 200,000 token window changes your workflow: Analyze 20+ files for a single refactor or review complex PR diffs in one pass.”
“I went from spending ~$90/month on Claude API calls to under $15 with GLM-5 and didn't notice a meaningful drop in quality.”
“Its hallucination rate is in the 30% range versus I don't know Gemini 3 Pro at 88%.”
“GLM-5 dropped before I could finish testing 4.7, and the reasoning jump is actually noticeable in everyday coding.”
“Zhipu releasing this under MIT is a massive move for the local LLM community.”
Related Videos
Watch tutorials, reviews, and discussions about GLM-5
“It's neck and neck with models like 5.2 codecs and Opus 4.5.”
“It is the first openweight model that I've successfully run a job that took over an hour for without issues.”
“Its hallucination rate is in the 30% range versus I don't know Gemini 3 Pro at 88%.”
“The reasoning density is significantly higher than GLM-4.”
“It basically replaces Claude 3.5 Sonnet for my internal coding tasks.”
“They literally doubled the almost doubled the number of parameters... all the way up to 744.”
“Even though it's a lot larger, it runs at pretty much if not faster than the older model.”
“Self-correction. Don't be condescending. Treat it like a valid question.”
“The sparse attention mechanism keeps memory usage low for such a big model.”
“Open-weight availability makes this the new champion for local hosting.”
“They created their own RL engine called Slime.”
“A 200,000 context window changes what enterprise AI even means.”
“It hits 77.8 on SWE-bench verified, beating Gemini 3 Pro at 76.2.”
“Zhipu AI is proving domestic hardware can train world-class models.”
“Agentic engineering is the key focus here, not just simple chat.”
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Pro Tips
Expert tips to help you get the most out of GLM-5 and achieve better results.
Activate Agentic Mode
Define multi-step plans in your prompts as GLM-5 is optimized for autonomous engineering rather than simple chat responses.
Local Hardware Allocation
Ensure significant VRAM or native Huawei Ascend hardware with the MindSpore framework is available for optimal throughput.
Implement Fallback Chains
Configure GLM-5 as your primary reasoning model with GLM-4.7-Flash as a cost-effective fallback for simpler instructions.
Use Structured Output
GLM-5 excels at generating precise .docx and .xlsx formats when given clear schema requirements for deliverables.
Testimonials
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Frequently Asked Questions
Find answers to common questions about GLM-5