This article analyzes the recent release of Zhipu AI’s GLM-5 and its direct competition with Anthropic’s Claude Opus 4.5, focusing on technical documentation admissions and the shift toward agentic engineering. We explore the architectural breakthroughs, benchmark results, and the unprecedented 128K output token limit that is redefining the AI landscape in 2026.
Is GLM-5 Equal to Claude Opus 4.5?
Yes, according to official documentation and recent SWE-bench results, GLM-5 has achieved performance parity with Claude Opus 4.5 in complex reasoning and systems engineering. GLM-5 features a massive 744B parameter Mixture-of-Experts (MoE) architecture and introduces a “crazy” 128K output token limit—vastly exceeding the 4K–8K limits of most frontier models. While Claude Opus 4.5 remains the “gold standard” for creative orchestration and human-like planning, GLM-5 has closed the gap in autonomous coding (scoring 77.8% on SWE-bench Verified) and long-horizon agentic tasks, often at a fraction of the inference cost.
The Era of Agentic Engineering: Breaking Down GLM-5
The release of GLM-5 by Zhipu AI (internationally known as Z.ai) marks a pivotal moment in the AI arms race. For months, rumors circulated about a “Claude 4.5 killer” emerging from Beijing. With the official documentation now public, the industry is witnessing a shift from “Vibe Coding”—where users prompt for snippets—to “Agentic Engineering,” where models manage entire repositories and complex business cycles.
1. Architectural Prowess: The 744B MoE Giant
GLM-5 is built on a sophisticated Mixture-of-Experts (MoE) framework that allows it to scale intelligence without becoming computationally prohibitive.
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Parameter Scale: The model boasts 744 billion total parameters, but only activates approximately 40 billion parameters per token. This ensures high-density intelligence with efficient throughput.
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DeepSeek Sparse Attention (DSA): By integrating DSA, GLM-5 significantly reduces deployment costs and memory overhead, allowing for better long-context management than its predecessor, GLM-4.7.
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Training Data: The model was trained on 28.5 trillion tokens, a substantial increase from previous iterations, focusing specifically on repo-level code and multi-step reasoning trajectories.
2. The 128K Output Limit: A Paradigm Shift
Perhaps the most “controversial” and exciting feature in the GLM-5 documentation is the 128,000 output token limit.
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Why it matters: Most frontier models (including Claude and GPT-4 series) can read large contexts but are limited in what they can write (usually 4,096 to 16,384 tokens).
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Complex Outputs: A 128K output limit allows GLM-5 to generate entire software modules, 50-page technical whitepapers, or complete architectural blueprints in a single pass without “forgetting” or cutting off mid-sentence.
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Agentic Continuity: This allows for long-horizon tasks, such as the “Vending Bench 2” simulation, where the model manages a business over a simulated year, achieving results that rival Claude Opus 4.5.
3. Benchmark Performance: The Data Points
The documentation “admits” parity through several key industry-standard tests. Below is how the frontier models stack up in early 2026.
Comparative Performance Table: GLM-5 vs. Competition
| Metric | GLM-5 (Z.ai) | Claude Opus 4.5 (Anthropic) | GPT-5.2 (OpenAI) |
| SWE-bench Verified (Coding) | 77.8% | 80.9% | 80.0% |
| Output Token Limit | 128,000 | 8,192 (Est.) | 4,096 (Standard) |
| Total Parameters | 744B (MoE) | Undisclosed | Undisclosed |
| HLE (Reasoning) | 50.2 | 52.1 | 51.5 |
| Primary Advantage | Agentic Engineering / Cost | Orchestration / Planning | Multimodal / Consistency |
| Compute Basis | Huawei Ascend (Non-Nvidia) | Nvidia H100/H200 | Nvidia H100/H200 |
EEAT Principles: Why the GLM-5 Documentation is Trustworthy
To understand the authority behind these claims, one must look at the “Experience, Expertise, Authoritativeness, and Trustworthiness” (EEAT) of the development team at Zhipu AI.
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Academic Heritage: Zhipu AI originated from the Knowledge Engineering Group (KEG) at Tsinghua University, one of the world's leading AI research institutions.
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Hardware Independence: GLM-5 was trained entirely on Huawei Ascend processors using the MindSpore framework. This proves that high-tier AI performance is no longer dependent on US-restricted Nvidia hardware, a major milestone for global AI sovereignty.
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Open Source Commitment: By releasing versions of their models under the MIT license, Zhipu has allowed the global community to verify their benchmarks independently, fostering a high level of transparency.
Key Features of GLM-5 for Developers and Enterprises
If you are an engineer or a business leader deciding between Claude Opus 4.5 and GLM-5, consider these factors:
From Chat Mode to Agent Mode
GLM-5 introduces two distinct operating states:
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Chat Mode: Optimized for speed, interactive dialogue, and lightweight tasks.
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Agent Mode: Designed for “Thinking” and “Doing.” In this mode, the model utilizes diverse tools (web browsing, terminal execution, file manipulation) to deliver results directly rather than just providing text advice.
Long-Horizon Planning
In the “Vending Bench 2” test, GLM-5 had to manage a simulated business. It demonstrated:
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Resource Management: Allocating funds for stock and repairs.
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Strategic Adjustment: Changing pricing based on simulated demand.
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Success Metric: It finished with a final account balance of $4,432, placing it at the very top of open-source models and within striking distance of Claude Opus 4.5.
Hardware and Deployment Efficiency
Because GLM-5 uses the “Slime” RL framework and DeepSeek Sparse Attention, it is significantly cheaper to run than proprietary US models. Developers are reporting that they can achieve “Sonnet-level” or “Opus-level” results for approximately 1/10th of the API cost.
The Reddit Verdict: Community Insights
In the r/AIToolsPerformance and r/LocalLLaMA communities, users have noted that while Claude Opus 4.5 still has a slight edge in “creative nuance” and “vibe coding,” GLM-5 is the superior choice for Systems Engineering.
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Pro Tip: Users on Reddit suggest using Claude Opus 4.5 for the initial high-level architecture and then switching to GLM-5 for the heavy lifting—writing thousands of lines of code—to take advantage of the 128K output limit.
Frequently Asked Questions (FAQ)
1. Is GLM-5 truly open source?
Zhipu AI typically releases its weights under the MIT license for research and commercial use, though the specific 744B “Flagship” model is currently rolling out via their “Max” API plan first, with open-source weights expected to follow.
2. How does the 128K output limit change AI usage?
It eliminates the need for “chunking.” Instead of asking an AI to write one function at a time, you can ask it to write an entire backend service, including documentation and test suites, in one single prompt.
3. Can I run GLM-5 locally?
Due to its 744B parameter size, running the full model locally requires massive VRAM (multiple H100s or large Mac Studio clusters). However, quantized versions (Int4/FP8) are expected to be compatible with high-end consumer hardware and specialized domestic chips like those from Moore Threads.
4. Does GLM-5 support English as well as Chinese?
Yes. While developed in China, the model is trained on a massive global dataset. Benchmarks show it is highly competitive in English-language coding and reasoning, often outperforming Llama-3 and equaling Claude in bilingual tasks.
5. What is “Agentic Engineering”?
Unlike standard coding (writing snippets), Agentic Engineering involves the AI acting as a semi-autonomous developer—identifying bugs, browsing documentation for library updates, and executing terminal commands to verify its own work.
