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.
Zhipu AI
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.
745B
Total Parameters
44B
Active (MoE)
Feb 2026
Launch
MIT
License (Expected)
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. Launching in February 2026, GLM-5 represents a generational leap in AI capability — featuring approximately 745 billion total parameters in a Mixture of Experts architecture with 44 billion active parameters per inference. It is engineered from the ground up 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 rapidly established itself as a leader in open-source AI research. The company completed a landmark Hong Kong IPO on January 8, 2026, raising approximately HKD 4.35 billion — funding that has directly accelerated GLM-5's development and positioned Zhipu AI for sustained investment in next-generation model architectures.
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. This positions GLM-5 not only as a technical achievement but as a milestone in China's drive toward self-reliant AI infrastructure — and a direct challenger to models such as OpenAI's GPT-5 and Anthropic's Claude.
Core Capabilities
GLM-5 delivers substantial advancements across five critical domains, each designed to push the boundaries of what large language models can achieve.
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, enabling it to process and reason over extensive documents, research papers, codebases, and even video transcripts in a single session.
Technical Architecture
GLM-5 employs a Mixture of Experts (MoE) architecture with approximately 745 billion total parameters and 44 billion active parameters per inference — roughly twice the scale of its predecessor GLM-4.5 (355 billion total parameters).
The model incorporates DeepSeek's sparse attention mechanism (DSA) for efficient handling of long contexts, similar to the approach used in DeepSeek-V3. This enables GLM-5 to process extended sequences without the computational overhead of traditional dense attention.
Trained entirely on Huawei Ascend chips using the MindSpore framework, GLM-5 achieves full independence from US-manufactured semiconductor hardware — a strategically important capability that demonstrates the viability of China's domestic AI compute stack at frontier scale.
Model Specifications
Architecture
Mixture of Experts (MoE)
Total Parameters
~745 Billion
Active Parameters
~44 Billion
Attention
DeepSeek Sparse (DSA)
Training Hardware
Huawei Ascend
Framework
MindSpore
Predecessor
GLM-4.5 (355B / 12B active)
GLM-5 vs GPT-5: Comparative Analysis
How GLM-5 compares to OpenAI's GPT-5 across key dimensions — from architecture and pricing to availability and training independence.
| Aspect | GLM-5 | GPT-5 |
|---|---|---|
| Status | Launching February 2026 | Released August 2025 (iterating) |
| Total Parameters | ~745B (MoE, 44B active) | Undisclosed (est. trillions-scale) |
| Architecture | MoE + Sparse Attention (DSA) | Unified router, multimodal |
| Context Length | 128K+ tokens (expected) | 400K input / 128K output |
| Reasoning | Frontier-level, multi-step | SOTA with thinking modes |
| Training Hardware | Huawei Ascend (US-independent) | NVIDIA / Azure |
| Pricing | Expected ultra-low cost / open-weight | $1.25/M input, $10/M output |
| Availability | API + likely open-weight (MIT) | API only (closed-source) |
Direct benchmark comparisons are pending GLM-5's official release. For reference, GLM-4.5 averages 63.2 across 12 standard benchmarks, competing closely with GPT-5 in coding and reasoning tasks.
Open Source and Pricing
Zhipu AI has a strong track record of open-sourcing its models. GLM-4.7, the current flagship, 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 of the GLM series. GLM-4.x API pricing sits at approximately $0.11 per million tokens — a fraction of GPT-5's $1.25 per million input tokens and $10 per million output tokens. GLM-5 is expected to maintain or improve upon this pricing advantage, making frontier-level AI capabilities accessible to a far broader range of developers and organizations.
GLM-5 is anticipated to be available as an open-weight model under the MIT license, making frontier-level AI accessible to developers and researchers worldwide — potentially disrupting the market in the same way that LLaMA and Mistral have reshaped the open-source AI landscape.
Release Timeline
Zhipu AI completes Hong Kong IPO, raising approximately HKD 4.35 billion to fund next-generation model development.
GLM-5 training nears completion on Huawei Ascend infrastructure. Internal testing and evaluation begin.
GLM-5 official launch anticipated, coinciding with the Lunar New Year period.
API access and potential open-weight release under MIT license expected to follow.
Frequently Asked Questions
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 expected to launch between February 10 and 15, 2026, coinciding with the Lunar New Year period. API access and a potential open-weight release are anticipated 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, directly funding GLM-5's development.
How does GLM-5 compare to GPT-5?
GLM-5 aims to match or exceed GPT-5 in reasoning and agentic tasks while offering significantly lower pricing and potential open-weight access under an MIT license. Its predecessor GLM-4.5 already competes closely with GPT-5 in coding and reasoning benchmarks, averaging 63.2 across 12 standard evaluations.
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 demonstrates the viability of China's compute stack at frontier scale.
What are GLM-5's key capabilities?
GLM-5 excels in five core areas: creative writing with stylistic versatility, advanced code generation and debugging, frontier-level multi-step reasoning, agentic intelligence with autonomous planning and tool use, and long-context processing for handling extensive documents and research materials.