How Does GLM-4.5 Surpass o3, Gemini 2.5 Pro, and Grok 4 with 90% Success in Agentic Benchmarks?

GLM-4.5 from Z.ai is emerging as a strong open-source contender in AI, excelling in tasks that demand reasoning, coding, and agentic skills. It claims a 90% success rate in agentic benchmarks, outpacing models like o3, Gemini 2.5 Pro, and Grok 4. This piece covers its key features, performance data, and why it stands out.

What is GLM-4.5 and Its Key Advantages?

GLM-4.5 serves as Z.ai's advanced large language model, designed for intelligent agent applications. With 355 billion parameters and only 32 billion active at once, it balances power and efficiency. It supports a huge 128,000-token context window, allowing it to handle long documents and complex conversations seamlessly.

Hybrid thinking mode for in-depth problem-solving
Non-thinking mode for quick responses
Native function calling for tool integration
Full open-source access on platforms like Hugging Face
A lighter version, GLM-4.5-Air, with 106 billion parameters for easier setups

This setup makes GLM-4.5 versatile for tasks from coding to research.

Inside GLM-4.5's Architecture

GLM-4.5 uses a Mixture-of-Experts design, activating only needed parameters per query. This hybrid system switches between deep reasoning for tough problems and fast answers for simple ones. Here's a quick comparison with competitors:

Feature	GLM-4.5	GLM-4.5-Air	DeepSeek R1	Grok 4
Total Parameters	355B	106B	236B	~320B
Active Parameters	32B	12B	122B	N/A
Context Window	128,000 tokens	128,000 tokens	64,000 tokens	256,000 tokens
Architecture	Mixture of Experts	MoE	MoE	Proprietary
Open Source	Yes (MIT)	Yes	Yes	No

This architecture boosts efficiency, making it ideal for practical applications.

Benchmark Performance

In tests across 12 global benchmarks, GLM-4.5 ranks third overall, beating DeepSeek and others in key areas. It shines in agentic tasks with a 90.6% success rate and coding scenarios.

Benchmark	GLM-4.5	DeepSeek R1	Grok 4	Gemini 2.5 Pro	Claude 4 Opus
Coding: LIVECode	72.9	77.0	81.9	80.1	63.6
Reasoning: MMLU	84.6	84.9	86.6	86.2	87.3
Math: MATH 500	98.2	98.3	99.0	96.7	98.2
Tool Use (Agentic)	90.6%	89.1%	92.5%	86%	89.5%

These results show GLM-4.5's strength in real-world coding and agent tasks, making it a top pick for developers.

Cost and Accessibility

GLM-4.5 keeps costs low, with pricing at $0.11 for input and $0.28 for output per million tokens. Compare that:

Model	Input (USD/million)	Output (USD/million)
GLM-4.5	$0.11	$0.28
DeepSeek R1	$0.14	$2.19
GPT-4 API	$10.00	$30.00

It runs on just eight Nvidia H20 GPUs, easing entry for startups and individuals.

Agentic Capabilities and Use Cases

Built for autonomous agents, GLM-4.5 handles function calling, multi-step planning, and debugging. Real applications include:

Creating coding assistants
Analyzing documents like contracts
Supporting game development
Running scientific simulations
Integrating into enterprise tools

Experts praise its reliability, with Z.ai's CEO noting it sets new standards for open and affordable AI.

Why GLM-4.5 Matters in AI Development

As an open-source model under MIT license, GLM-4.5 promotes global access and community involvement. Unlike closed models, it allows full control and local deployment, fostering innovation.