| 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% |
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.
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.
