GLM-5 is Now Available on GMI Cloud

The industry is shifting from models that “write code” to models that “build systems.” GLM-5 is the first open-source model designed for this new era.

GLM-5, the newest frontier model from Zhipu AI's BigModel project, is now live on GMI Cloud. Our internal testing confirms this isn't just another general-purpose model; it's a specialized tool built for discerning, top-tier programmers who think at the system level, not just the function level. It leverages a massive 745B-parameter Mixture-of-Experts (MoE) architecture, activating a 44B-parameter subset of experts for each token. This design provides the nuance of a massive model while maintaining the performance of a much smaller one, a concept well-explained in Hugging Face's MoE overview.

This announcement covers our team's findings on what you can do with GLM-5 today, its real-world performance on our platform, and how it compares to other frontier models.

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Are You Facing These Engineering Challenges?

• Endless Debugging Cycles: Are you spending days hunting down race conditions or performance bottlenecks in distributed systems?
• Stalled Refactoring Projects: Is that monolith-to-microservices migration stuck because of the sheer complexity of the codebase?
• High Cost of Intelligence: Are you paying a premium for Claude Opus 4.6 to handle complex reasoning, and it's eating into your margins?

If so, GLM-5 was built for you. Our internal testing confirms its value is not just raw power, but reliability. The most common feedback from our platform engineers was that the model required minimal supervision during long-running, autonomous tasks.

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What GLM-5 Does: Production-Grade Capabilities

GLM-5 is designed for depth and complexity. Here's what our internal testing revealed:

1. It Performs Surgical Code Modifications. Like a senior architect, GLM-5 excels at making targeted changes to existing code, preserving context and saving our engineers valuable time. We gave it complex, multi-file codebases and high-level objectives, and it not only wrote the code but also iteratively debugged it after compilation and runtime failures, analyzing logs to pinpoint root causes and fix stubborn bugs until the system ran.

2. It Executes Long-Horizon Agentic Workflows. The model's MoE architecture provides superior long-form stability, with fewer instances of context drift or coherence lapses during multi-step reasoning. This allowed our team to maintain goal alignment over hours-long, multi-step tasks, making it ideal for complex DevOps automation or infrastructure provisioning.

3. Deep Logic Over Surface Aesthetics. Unlike models optimized for front-end polish and surface-level code generation, GLM-5 prioritizes backend architecture design, complex algorithm implementation, and deep debugging. It's built for the hard problems that live beneath the UI layer.

4. Opus-Level Intelligence, Open-Source Freedom. GLM-5 directly benchmarks against Claude Opus 4.5 in code logic density and systems engineering capability, while providing the deployment freedom and cost-effectiveness that only open-source can deliver.

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Our Internal Performance Benchmarks

Metric — First-Token Latency
Performance on GMI Cloud: < 1 sec (short prompts)
What it means for you: Immediate feedback for interactive sessions and rapid development cycles.

Metric — Sustained Throughput
Performance on GMI Cloud: 30–60 tokens/sec
What it means for you: Consistent output speed for large-scale code generation or document analysis.

Metric — Context Stability
Performance on GMI Cloud: Coherent at 8K–16K+ tokens
What it means for you: Maintains focus and accuracy across large documents or entire code repositories.

‍‍These metrics confirm that GLM-5 is responsive enough for interactive workflows and robust enough for large, batch-oriented jobs on our platform.

How GLM-5 Stacks Up: A Practical Comparison

The release of Claude Opus 4.6 and GPT-5.3 Codex signals a clear industry shift: programming models are evolving from ‘can write code’ to ‘can build systems.’ Discerning engineers are no longer just evaluating output quality—they’re prioritizing agentic depth and systems engineering capability.

GLM-5 is the first open-source model built for this new paradigm.

No model is best for everything. Here's our guide to choosing the right tool for the job, based on our internal tests:

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Model Selection Guide Based on Use Case

Use Case — Complex Backend Refactoring & Debugging
Recommended model: GLM-5
Why: Unmatched reasoning depth and self-correction. Excels at targeted code modification.

Use Case — General Application & UI Development
Recommended model: Qwen3 / GPT-5
Why: Qwen3 offers fast coding variants and strong tool usage; GPT-5 has the broadest ecosystem support.

Use Case — High-Volume, Low-Complexity Tasks
Recommended model: DeepSeek R1
Why: Optimized for lightweight, economical queries where reasoning depth is secondary.

Use Case — Sensitive Content & Safety Scaffolding
Recommended model: Claude Opus 4.6
Why: Strong safety behavior and conservative outputs for high-stakes content.

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Start Building with GLM-5 Today

Stop wrestling with complex systems and let your AI do the heavy lifting. GLM-5 is more than just a new model—it's a new class of tool for a new era of software engineering.

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Try GLM-5 or other models on GMI Cloud →

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