GLM-5 and GLM-5-Turbo: Z.ai's Revolution in Agentic AI and Advanced Programming Models

Introduction

In February 2026, Zhipu AI (operating under the Z.ai brand) launched GLM‑5, its new flagship model that marks a qualitative leap in the world of large language models. Just one month later, in March 2026, it released the specialized GLM‑5‑Turbo version. These two models are not mere routine updates; they reflect a strategic shift from “vibe coding” to “agentic engineering”.

GLM‑5 is fully open‑source under the MIT license, while GLM‑5‑Turbo focuses on practical performance in agentic scenarios such as OpenClaw. This article is suitable for beginners who want to understand the basics of the new models, professionals looking for precise benchmarks, and entrepreneurs considering integrating these models into real‑world applications. We will cover technical specifications, performance, availability, and practical applications without exaggeration or speculation.

What is GLM‑5?

GLM‑5 is Z.ai’s new main model, officially released on February 12, 2026 (with preliminary announcements on February 11). It is designed specifically for complex system‑engineering tasks and long‑horizon agentic tasks. It builds on the development of previous GLM series (such as GLM‑4.5 and GLM‑4.7) but significantly surpasses them in programming and agentic capabilities.

The model uses an efficient Mixture‑of‑Experts (MoE) architecture, with total parameters reaching 744 billion, but only 40 billion active during inference. Compared to GLM‑4.5 (355 billion parameters, 32 billion active), this represents a substantial increase in size. Pre‑training data volume also rose from 23 trillion tokens to 28.5 trillion tokens.

GLM‑5 integrates DeepSeek Sparse Attention (DSA) technology, which reduces deployment costs while preserving long‑context capability. Z.ai also developed a new RL architecture called “slime” (available on GitHub: https://github.com/THUDM/slime) to improve post‑pretraining training efficiency, allowing more accurate and faster iterations.

Key Technical Specifications of GLM‑5

• Parameters: 744B total (40B active).
• Training data: 28.5 trillion tokens.
• Context length: 200,000 tokens.
• Maximum output: 128,000 tokens.
• Input/output: Text only (text‑only).
• Compatibility: Runs locally on vLLM and SGLang, supports non‑NVIDIA chips (Huawei Ascend, Moore Threads, etc.) via optimization and quantization.

These specifications make GLM‑5 suitable for wide‑scale deployment without relatively high costs.

Performance and Comparisons: Precise Benchmarks

GLM‑5 achieves leading performance among open‑source models in reasoning, programming, and agentic tasks. It approaches leading closed models such as Claude Opus 4.5 in some programming scenarios, and outperforms other open models like DeepSeek‑V3.2 and Kimi K2.5 in most cases.

Here is a selected comparison table from official benchmarks (source: Z.ai official blog):

Benchmark	GLM‑5	GLM‑4.7	DeepSeek‑V3.2	Kimi K2.5	Claude Opus 4.5	Gemini 3.0 Pro
Humanity’s Last Exam (w/ Tools)	50.4	42.8	40.8	51.8	43.4	45.8
AIME 2026 I	92.7	92.9	92.7	92.5	93.3	90.6
GPQA‑Diamond	86.0	85.7	82.4	87.6	87.0	91.9
SWE‑bench Verified	77.8	73.8	73.1	76.8	80.9	76.2
Terminal‑Bench 2.0	56.2	41.0	39.3	50.8	59.3	54.2
BrowseComp (w/ Context)	75.9	67.5	67.6	74.9	67.8	59.2
Vending Bench 2 (dollars)	$4,432	$2,377	$1,034	$1,198	$4,967	$5,478

GLM‑5 stands out in Vending Bench 2 (a year‑long simulated agentic business‑management task), achieving the highest score among open‑source models. It also reduces the hallucination rate to its lowest levels according to Artificial Analysis.

Real‑World Practical Applications of GLM‑5

1. Agentic Programming: A developer can use GLM‑5 with tools like Claude Code or OpenClaw to build a complete web application from a text description. Example: “Create an e‑commerce app that supports payment and delivery with a database.” The model generates front‑end and back‑end code, tests it, and corrects errors across multiple cycles.

2. Complex System Engineering: In a corporate project, GLM‑5 can automatically create PRD documents, Excel spreadsheets, and PDF reports via the Agent Mode available on chat.z.ai.

3. Long‑Term Tasks: Example for beginners: ask the model “Plan a full six‑month marketing campaign for a mobile app with a timeline and budget.” The model maintains context and generates a coherent plan.

GLM‑5‑Turbo: The Agent‑Optimized Variant

GLM‑5‑Turbo was released on March 16, 2026 as Z.ai’s first closed‑source model (previously under the test name Pony‑Alpha‑2). It is not a replacement for GLM‑5 but a complement: trained from scratch for OpenClaw scenarios (an open agent framework that works across applications and devices).

Key Specifications of GLM‑5‑Turbo:

• Context length: 200,000 tokens (same as GLM‑5).
• Maximum output: 128,000 tokens.
• Improvements: more precise tool calling (low tool‑calling error rate), better decomposition of complex instructions, improved temporal understanding of scheduled and ongoing tasks, highly efficient execution of long chains.

Quick Comparison between GLM‑5 and GLM‑5‑Turbo:

Aspect	GLM‑5	GLM‑5‑Turbo
Availability	Open‑source (MIT) + API	API only (not open‑source)
Focus	General agentic engineering + programming	OpenClaw + long execution chains
Cost (per million tokens)	Input $1, Output $3.2	Input $1.2, Output $4
Performance in agents	Excellent generally	Superior in stability and execution

Practical Applications of GLM‑5‑Turbo

• Real‑Business Automation: Example: “Monitor product prices daily, send alerts via API, and update a database automatically every hour.” Turbo maintains execution without interruption.
• Integration with OpenClaw: Serves as the native execution engine for agents that interact with applications and devices, making it ideal for building advanced automation systems in enterprises.

How to Access and Deploy

• GLM‑5:

  • Local download: Hugging Face (https://huggingface.co/zai-org/GLM-5) or ModelScope.
  • Free trial: chat.z.ai (automatically selects GLM‑5).
  • API: api.z.ai (compatible with OpenAI SDK).
  • Coding Plan (Pro/Max) for full access with programming tools.

• GLM‑5‑Turbo: API only via https://api.z.ai. Available on OpenRouter and other platforms. Recommended for projects requiring high agent stability.

Conclusion and Final Message

GLM‑5 and GLM‑5‑Turbo represent real progress in turning AI into a practical engineering tool, not just a conversational interface. GLM‑5 opens the door for developers and small businesses via open access, while Turbo provides enhanced performance for daily production tasks.

For entrepreneurs: try integrating these two models into your applications now—whether locally or globally—because the ability to build stable intelligent agents will be a decisive competitive factor in 2026 and beyond. Do not rely on marketing promises; test them yourselves on real benchmarks and practical tasks.

Official accurate links:

• GLM‑5 blog: https://z.ai/blog/glm-5
• API documentation: https://docs.z.ai
• Hugging Face: https://huggingface.co/zai-org/GLM-5

These two models are not the end of the race, but the beginning of a new era of agentic engineering. Start today to build the future.