GLM 5
Next-Generation Large Language Model
745 billion parameters. 44 billion active. Built from the ground up for agentic intelligence, advanced reasoning, and frontier-level performance across coding, creative writing, and complex problem-solving.
What Is GLM-5?
GLM-5 is the fifth-generation large language model developed by Zhipu AI (Z.ai), one of China's foremost artificial intelligence companies. It represents a generational leap in AI capability — featuring approximately 745 billion total parameters in a Mixture of Experts (MoE) architecture with 256 experts, 8 activated per token (5.9% sparsity), and 44 billion active parameters per inference. GLM-5 is engineered for agentic intelligence, advanced multi-step reasoning, and frontier-level performance across coding, creative writing, and complex problem-solving.
Zhipu AI, founded in 2019 as a spin-off from Tsinghua University, has established itself as a leader in open-source AI research. The company completed a Hong Kong IPO in January 2026, raising approximately HKD 4.35 billion (USD $558 million), funding that has directly accelerated GLM-5's development. In a strategically significant move, GLM-5 has been trained entirely on Huawei Ascend chips using the MindSpore framework, achieving full independence from US-manufactured hardware — positioning GLM-5 as both a technical achievement and a milestone in self-reliant AI infrastructure.
Overview
At a Glance
Coding
Full lifecycle development partner with strong code generation, debugging, and multi-language support.
Agentic Intelligence
Autonomous planning, tool use, web browsing, and multi-step workflows with minimal human intervention.
200K Context
Process extensive documents, codebases, and research in one session with efficient sparse attention.
Open & Cost-Efficient
Expected MIT license; API pricing a fraction of GPT-5 and Claude. Hugging Face and ModelScope.
Core Capabilities
What GLM-5 Delivers
Substantial advancements across five critical domains, each designed to push the boundaries of what large language models can achieve.
Creative Writing
GLM-5 generates high-quality, nuanced creative content with stylistic versatility — from long-form narrative and technical documentation to marketing copy and academic prose.
Coding
With significant advances in code generation, debugging, and multi-language comprehension, GLM-5 serves as a powerful development partner for software engineers across the full development lifecycle.
Advanced Reasoning
GLM-5 achieves frontier-level multi-step logical reasoning and complex problem-solving, enabling it to tackle mathematical proofs, scientific analysis, and intricate analytical tasks.
Agentic Intelligence
A core differentiator of GLM-5 is its built-in agentic architecture — designed for autonomous planning, tool utilization, web browsing, and multi-step workflow management with minimal human intervention.
Long-Context Processing
GLM-5 handles massive context windows (up to 200K tokens), enabling it to process and reason over extensive documents, research papers, codebases, and even video transcripts in a single session.
Use Cases
Where GLM-5 Shines
End-to-End Development
From requirements to deployment — full-stack and backend development with one model across the lifecycle.
AI Agents & Automation
Build assistants that plan, browse, call tools, and manage multi-step workflows over long sessions.
Documentation & Reports
Generate technical documentation, marketing copy, and structured reports directly from prompts.
Research & Analysis
Reason over long research papers, codebases, and complex analytical tasks with 200K context.
Technical Architecture
How GLM-5 Is Built
GLM-5 employs a Mixture of Experts (MoE) architecture with approximately 745 billion total parameters, featuring 256 experts with 8 activated per token (5.9% sparsity) and 44 billion active parameters per inference — roughly twice the scale of its predecessor GLM-4.5. The model incorporates DeepSeek's sparse attention mechanism (DSA) for efficient long-context handling, enabling processing of sequences up to 200K tokens without the computational overhead of traditional dense attention. Trained entirely on Huawei Ascend chips using MindSpore, GLM-5 achieves full independence from US-manufactured semiconductor hardware.
| Total Parameters | ~745 Billion |
| Active Parameters | ~44 Billion |
| Expert Configuration | 256 total / 8 active (5.9%) |
| Context Window | Up to 200K tokens |
| Attention | DeepSeek Sparse (DSA) |
| Training Hardware | Huawei Ascend |
Why GLM-5
Competitive Edge
GLM-5 shows competitive performance against Claude Opus series and GPT-5 across reasoning, coding, and agentic tasks. Benchmarks demonstrate improvements over GLM-4.7 in creative writing and multimodal domains.
- ✓ Frontier-level multi-step reasoning and agentic intelligence with autonomous planning and tool use.
- ✓ 200K token context window with efficient sparse attention — strong balance between capability and deployment cost.
- ✓ Trained on Huawei Ascend (US-independent); expected MIT-licensed open weights for commercial deployment and fine-tuning.
- ✓ Cost-efficient API pricing (GLM-4.x ~$0.11/M tokens); GLM-5 expected to maintain or improve this advantage versus GPT-5 and Claude.
Open Source & Pricing
Access and Cost
Zhipu AI has a strong track record of open-sourcing its models. GLM-4.7 is freely available on Hugging Face for commercial use. GLM-5 is anticipated to follow this precedent, with an expected release under the MIT license — enabling unrestricted commercial deployment, fine-tuning, and community-driven research.
Cost efficiency remains a core advantage. GLM-4.x API pricing sits at approximately $0.11 per million tokens — a fraction of GPT-5's $1.25/M input and $10/M output. GLM-5 is expected to maintain or improve upon this pricing advantage, making frontier-level AI accessible to a broader range of developers and organizations.
Release Timeline
Key Milestones
- Jan 8, 2026 — Zhipu AI completes Hong Kong IPO, raising ~HKD 4.35B (USD $558M) to fund next-generation model development.
- Jan 2026 — GLM-5 training nears completion on Huawei Ascend; internal testing and evaluation begin.
- Mid-Feb 2026 — GLM-5 becomes accessible via Z.ai platform and WaveSpeed API, with competitive benchmarks against Claude Opus series.
- Q1 2026 — Open-weight release under MIT license expected to follow initial API launch.
Get Started
How to Use GLM-5
API Access
Use Zhipu AI's Z.ai platform or WaveSpeed API for immediate access to GLM-5. Integrate into your applications with standard API calls.
Open Weights
Download model weights from Hugging Face or ModelScope when the open-weight release is available. Expected under MIT license for self-hosting and fine-tuning.
Deploy
Run on Huawei Ascend, Moore Threads, Cambricon, or standard GPU cloud infrastructure. Full documentation and examples on official channels.
Frequently Asked Questions
FAQ
What is GLM-5?
GLM-5 is the fifth-generation large language model developed by Zhipu AI, featuring approximately 745 billion parameters in a Mixture of Experts (MoE) architecture with 44 billion active parameters. It is designed for advanced reasoning, coding, creative writing, and agentic intelligence — representing a significant leap over its predecessor GLM-4.5.
When will GLM-5 be released?
GLM-5 is now accessible via Z.ai's platform and WaveSpeed API as of mid-February 2026. The model is positioned with competitive performance against frontier models. Open-weight release under MIT license is expected to follow in Q1 2026.
Who developed GLM-5?
GLM-5 is developed by Zhipu AI (Z.ai), a leading Chinese AI company that spun out of Tsinghua University in 2019. In January 2026, Zhipu AI completed a Hong Kong IPO raising approximately HKD 4.35 billion (USD $558 million), directly funding GLM-5's development.
How does GLM-5 compare to GPT-5?
GLM-5 aims to match or exceed GPT-5 and Claude Opus in reasoning and agentic tasks while offering significantly lower pricing and potential open-weight access. It demonstrates improvements over GLM-4.7 in creative writing and multimodal tasks, with a 200K token context window and training fully on Huawei Ascend for hardware independence.
Will GLM-5 be open source?
Zhipu AI has a strong history of open-sourcing models — GLM-4.7 is freely available on Hugging Face. GLM-5 is anticipated to be released as an open-weight model under the MIT license, enabling free commercial use, fine-tuning, and community-driven development.
What hardware was used to train GLM-5?
GLM-5 was trained entirely on Huawei Ascend chips using the MindSpore framework, achieving full independence from US-manufactured semiconductor hardware. This represents a milestone in domestic AI infrastructure and the viability of China's compute stack at frontier scale.
Start with GLM-5
Access GLM-5 via Z.ai or WaveSpeed API today, or download open weights from Hugging Face and ModelScope when available. Explore documentation and integrate frontier-level AI into your products.
Get Started