GLM-4.5: An Open-Source Model With Enterprise-Grade Performance

In July 2025, Z.ai released GLM-4.5, which pairs large scale with practical usability through a Mixture-of-Experts (MoE) architecture. It is one of the strongest open-weight models released so far, and it widens the set of models worth evaluating for business work.

GLM-4.5 achieves exceptional performance with a score of 63.2, ranking 3rd place among all proprietary and open-source models, while its companion model GLM-4.5-Air delivers competitive results at 59.8 while maintaining superior efficiency. What makes this achievement remarkable is that both models are released under the MIT open-source license and can be used commercially and for secondary development.

Technical Excellence Meets Economic Disruption

Architectural Innovation Drives Performance

The flagship GLM-4.5 model has 355 billion total parameters with 32 billion active parameters, while the compact GLM-4.5-Air version offers 106 billion total parameters and 12 billion active parameters. This Mixture-of-Experts (MoE) architecture allows GLM-4.5 to have 355B total parameters while only activating 32B per inference, providing the knowledge capacity of a massive model with the efficiency of a smaller one, resulting in 8x better performance per computational cost compared to dense models of similar capability.

The model’s dual-mode operation represents a paradigm shift in AI interaction design. Both GLM-4.5 and GLM-4.5-Air are hybrid reasoning models, offering thinking mode for complex reasoning and tool using, and non-thinking mode for instant responses. This flexibility allows organizations to optimize both computational costs and user experience depending on task complexity.

Benchmark Performance That Rivals Industry Leaders

GLM-4.5’s performance metrics tell a compelling story of technical achievement. Comparing GLM-4.5 with various models from OpenAI, Anthropic, Google DeepMind, xAI, Alibaba, Moonshot, and DeepSeek on 12 benchmarks covering agentic (3), reasoning (7), and Coding (2), GLM-4.5 is ranked at the 3rd place and GLM-4.5 Air is ranked at the 6th.

In specialized domains, the model demonstrates exceptional capabilities:

Agentic Performance: The model leads in tool-calling reliability, with a success rate of 90.6%, edging out Claude 4 Sonnet. This level of reliability is crucial for autonomous agent applications where tool integration determines success rates.

Coding Excellence: GLM-4.5 scored 64.2% on SWE-bench coding, bettering even GPT-4.1 (48.6%), and in real-life coding challenges, GLM-4.5 wins 80.8% against Qwen3 Coder.

Extended Context: GLM-4.5 provides 128k context length and native function calling capacity, enabling complex document analysis and multi-turn conversations without context loss.

The Economics of AI Model Selection

Dramatic Cost Reductions Challenge Industry Norms

Perhaps the most disruptive aspect of GLM-4.5 isn’t its technical capabilities, but its pricing structure. GLM-4.5 offers an 87% drop in output token cost versus DeepSeek, while Western Benchmarks show OpenAI’s GPT-4 and Google’s Gemini range from USD 3-15 per million tokens, positioning GLM-4.5 as an order-of-magnitude cost reduction.

Current pricing across different providers shows significant variations:

• GLM-4.5 Input token price: $0.57, Output token price: $2.15 per 1M Tokens on some platforms
• On SiliconFlow: GLM-4.5 $0.5/M tokens (input) and $2/M tokens (output); GLM-4.5-Air $0.14/M tokens (input) and $0.86/M tokens (output)
• Z.ai’s official API calls cost as low as $0.2 per million input tokens and $1.1 per million output tokens

Hardware Accessibility Democratizes Deployment

GLM-4.5 requires just eight Nvidia H20 GPUs (export-compliant in China), slashing the hardware barrier for both researchers and startups. This accessibility extends to smaller deployments: for the Air version, if you have a 32-64GB GPU, you’re set.

Strategic Implications for Multi-Model AI Platforms

The Platform Advantage Becomes Critical

The release of GLM-4.5 underscores why unified AI platforms are becoming essential for modern businesses. With such dramatic performance and cost variations between models, organizations need the flexibility to switch between different AI providers based on specific use cases.

Consider the strategic advantages of platform-based approaches:

Right model for the task: Teams can send simple, high-volume queries to fast models like GLM-4.5-Air and keep the heaviest models for complex reasoning, so each request goes to the model that handles it best.

Risk Mitigation: Dependence on a single AI provider creates business risk. Multi-model platforms ensure continuity even when individual providers face capacity constraints or policy changes.

Performance Matching: Different models excel at different tasks. GLM-4.5 matches the performance of Claude 4 Sonnet on agent benchmarks and in web browsing tasks, GLM-4.5 gives correct answers for 26.4% of all questions, outperforming Claude-4-Opus (18.8%).

Team Collaboration and Cost Transparency

Traditional per-user pricing models become prohibitively expensive when scaling AI across organizations. GLM-4.5’s arrival strengthens the case for consumption-based pricing models where high-speed versions demonstrate generation speeds exceeding 100 tokens per second in real-world tests, supporting low-latency and high-concurrency deployment scenarios.

Teams using unified platforms can:

• Track actual token consumption across different models
• Set budget limits and usage policies by project or department
• Compare model performance and costs in real-time
• Switch models without changing integrations or workflows

Looking Forward: The Open-Source AI Renaissance

China’s Strategic AI Push Reshapes Global Competition

The release of GLM-4.5 arrives amid a surge of competitive open-source model launches in China, with Qwen releasing four new open-source LLMs in a single week. This acceleration reflects China’s state-supported rise in open LLM development, with over 1,500 Chinese LLMs launched in 2025 alone, and GLM-4.5’s “open-almost-everything” release reinforces China’s reputation as both an innovator and access champion.

Technical Innovation Continues

To facilitate highly efficient Reinforcement Learning (RL) training required for large-scale models, Z.ai developed and open-sourced slime, an RL infrastructure engineered for exceptional flexibility, efficiency, and scalability. This infrastructure approach suggests that model development capabilities are becoming as important as the models themselves.

Practical Implementation Considerations

Integration and Deployment

For organizations evaluating GLM-4.5, several deployment options exist:

Cloud-Based Access: GLM-4.5 and GLM-4.5-Air are available on Z.ai, Z.ai API and open-weights are available at HuggingFace and ModelScope. This provides immediate access without infrastructure investment.

Self-Hosting: Users can directly experience the model on Hugging Face or ModelScope or download the model for private deployment.

API Integration: Models support OpenAI-compatible APIs with full specifications available, enabling easy integration into existing workflows.

Performance Optimization

GLM-4.5 has half the number of parameters of DeepSeek-R1 and one-third that of Kimi-K2, yet it outperforms them on multiple standard benchmark tests, attributed to the higher parameter efficiency of GLM architecture. This efficiency translates to:

• Lower inference costs per request
• Faster response times for users
• Reduced infrastructure requirements
• Better scalability for high-volume applications

Conclusion: Navigating the New AI Economics

GLM-4.5 is a strong open-weight model under a permissive license, and it is one of many. New contenders now appear every few weeks, so the best model for a given task is a moving target.

The teams that get the most from that pace are the ones that can evaluate a new model on their own work quickly, and adopt it without rebuilding their tooling.

StickyPrompts puts open-weight models like GLM next to the frontier models from OpenAI, Anthropic and Google in one governed workspace, so your team can pick the best model for each job. Start a free workspace.