The landscape of Artificial Intelligence has shifted once again with the release of GLM-4.7. Positioned as a major leap forward in “Advancing the Coding Capability,” this new model from Z.ai (Zhipu AI) introduces significant improvements in agentic coding, complex reasoning, and tool usage.
For developers, data scientists, and enterprise users, the question is simple: How does GLM-4.7 stack up against its predecessor, GLM-4.6, and the current titans of the industry like Gemini 3 Pro and Claude Sonnet 4.5?
In this review, we break down the key features of GLM-4.7, analyze its “Vibe Coding” capabilities, and provide detailed benchmark comparisons to help you decide if it’s the right engine for your next project.
What is GLM-4.7? Key Features at a Glance
GLM-4.7 isn't just a minor patch; it is a substantial upgrade focused on making AI a more effective partner in complex workflows. According to the official Z.ai technical report, the model excels in three core areas:
- Core Coding & Agents: GLM-4.7 is designed to think before it acts. It supports Interleaved Thinking and Preserved Thinking, allowing it to maintain context across multi-turn coding sessions. This results in a massive 12.9% boost on SWE-bench Multilingual and a 16.5% boost on Terminal Bench 2.0.
- Vibe Coding (UI Quality): Beyond logic, GLM-4.7 understands aesthetics. It generates cleaner, modern webpages with better layouts, magnetic CTAs, and accurate sizing—moving away from generic “AI-generated” looks.
- Complex Reasoning: With a 12.4% increase in performance on the HLE (Humanity's Last Exam) benchmark, the model demonstrates a superior ability to solve difficult mathematical and logic problems compared to GLM-4.6.
Comparison 1: GLM-4.7 vs. GLM-4.6 (The Upgrade)
The most immediate comparison for current users is against the previous version. GLM-4.7 offers clear gains across the board, particularly in tasks requiring external tools and complex instruction following.
| Benchmark Category | Metric / Dataset | GLM-4.7 | GLM-4.6 | Improvement |
|---|---|---|---|---|
| Reasoning | HLE (Humanity's Last Exam) | 24.8% | 17.2% | +7.6% |
| HLE (w/ Tools) | 42.8% | 30.4% | +12.4% | |
| AIME 2025 (Math) | 95.7% | 93.9% | +1.8% | |
| Coding Agents | SWE-bench Verified | 73.8% | 68.0% | +5.8% |
| SWE-bench Multilingual | 66.7% | 53.8% | +12.9% | |
| Terminal Bench 2.0 | 41.0% | 24.5% | +16.5% | |
| General Agents | BrowseComp | 52.0% | 45.1% | +6.9% |
| τ²-Bench (Tool Use) | 87.4% | 75.2% | +12.2% |
Data Source: Z.ai GLM-4.7 Technical Report (2025)
Analysis: The jump in Terminal Bench 2.0 (+16.5%) and HLE w/ Tools (+12.4%) indicates that GLM-4.7 is significantly better at handling real-world environments where the AI needs to execute commands, browse the web, or use specific APIs to solve a problem.
Comparison 2: GLM-4.7 vs. The Giants (Gemini 3 Pro, Claude Sonnet 4.5, GPT-5.1)
How does GLM-4.7 compete on the global stage? The following table compares it against the heavy hitters: Gemini 3.0 Pro, Claude Sonnet 4.5, and GPT-5.1 High (referred to here as Pro/High tier).
While GLM-4.7 may not win every single metric, it proves to be a highly competitive alternative, especially in reasoning-heavy tasks where it often outperforms Claude Sonnet 4.5 and rivals the GPT-5 series.
| Benchmark | GLM-4.7 | Gemini 3.0 Pro | Claude Sonnet 4.5 | GPT-5.1 High |
|---|---|---|---|---|
| MMLU-Pro (Reasoning) | 84.3 | 90.1 | 88.2 | 87.0 |
| GPQA-Diamond (Expert QA) | 85.7 | 91.9 | 83.4 | 88.1 |
| HLE w/ Tools (Complex) | 42.8 | 45.8 | 32.0 | 42.7 |
| AIME 2025 (Math) | 95.7 | 95.0 | 87.0 | 94.0 |
| HMMT Feb 2025 (Math) | 97.1 | 97.5 | 79.2 | 96.3 |
| LiveCodeBench-v6 (Code) | 84.9 | 90.7 | 64.0 | 87.0 |
| SWE-bench Verified (Eng) | 73.8 | 76.2 | 77.2 | 76.3 |
| Terminal Bench 2.0 | 41.0 | 54.2 | 42.8 | 47.6 |
Note: “GPT-5.1 High” data is used for the GPT-5.1 comparison. “-” indicates data not available in the source.
Key Takeaways
- Math & Reasoning Parity: In the AIME 2025 benchmark, GLM-4.7 (95.7%) actually outperforms Gemini 3.0 Pro (95.0%) and GPT-5.1 High (94.0%), demonstrating world-class mathematical reasoning capabilities.
- Competitive Tool Use: On the HLE (w/ Tools) benchmark, GLM-4.7 scores 42.8%, effectively tying with GPT-5.1 High (42.7%) and beating Claude Sonnet 4.5 (32.0%) by a wide margin. This suggests GLM-4.7 is an excellent choice for agentic workflows involving complex problem-solving.
- Coding Efficiency: While Gemini 3.0 Pro leads in raw coding benchmarks like LiveCodeBench, GLM-4.7 remains a strong contender, particularly given its optimization for “Vibe Coding” (UI/Frontend generation), which benchmarks don't always capture fully.
Why “Vibe Coding” Matters
One of the standout features of GLM-4.7 is “Vibe Coding.” Traditional coding models often produce functional but ugly frontend code. GLM-4.7 has been tuned to produce “cleaner, more modern webpages” right out of the box.
- Better Defaults: High-contrast dark modes, bold typography, and magnetic CTAs.
- Less Iteration: Developers spend less time styling “ugly” boilerplate code.
Getting Started with GLM-4.7
GLM-4.7 is available now via multiple channels:
- Z.ai Platform: Use it directly in the chat interface or via API.
- Coding Agents: It is integrated into tools like Claude Code, Kilo Code, and Roo Code.
- Local Deployment: Weights are available on HuggingFace and ModelScope, with support for vLLM and SGLang.
Conclusion
GLM-4.7 represents a maturing of the AI ecosystem. It is no longer just about who has the highest generic score, but who handles tools, complex reasoning, and multilingual coding best. With its ability to outperform major competitors in mathematical benchmarks like AIME 2025 and its focus on high-quality UI generation, GLM-4.7 is a model that demands attention in 2025.
