# GLM-4.7: Advancing the Coding Capability

Source: https://www.youtube.com/watch?v=KvW3K6eQj9s
Recap page: https://rapidrecap.app/video/KvW3K6eQj9s
Generated: 2025-12-24T21:02:48.747+00:00

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## Quick Overview

The release of the GLM-4.7 large language model marks a significant advancement, achieving a 73.8% success rate on the SWUE benchmark, substantially outperforming its predecessor, GLM-4.6, by 5.8 percentage points, with key improvements stemming from a new architecture that enhances long-term memory and stability, making it particularly effective for complex, multi-turn reasoning and agentic tasks.

**Key Points:**
- GLM-4.7 achieved a 73.8% success rate on the SWUE benchmark, a 5.8% gain over GLM-4.6.
- The model scored 95.7% on the HLE benchmark, significantly beating the older Gemini 3.0 Pro score of 90.5%.
- The core architectural change involves 'preserved thinking'—reusing internal reasoning blocks across turns—which boosts stability and efficiency.
- This new architecture allows agents to handle complex, multi-turn tasks like fixing bugs or creating new features with less information loss.
- The model demonstrates strong tool-using capability, scoring 52.0% on web browsing, indicating better external interaction.
- The focus shifts from raw coding performance (which is still high at 84.4% on the coding benchmark) to superior general reasoning, integration, and control.

![Screenshot at 00:13: The introduction of the GLM-4.7 research report, which promises to detail the utility of the new model as a reliable high-performance coding partner.](https://ss.rapidrecap.app/screens/KvW3K6eQj9s/00-00-13.jpg)

**Context:** The video discusses the release and performance evaluation of the GLM-4.7 large language model, positioning it as the next major step in creating reliable, high-performance AI coding partners. The analysis hinges on comparing GLM-4.7's performance against its predecessor (GLM-4.6) and other major models on various benchmarks focusing on reasoning, coding, and agentic capabilities.

## Detailed Analysis

The discussion centers on the GLM-4.7 research report, presenting it as a major upgrade over GLM-4.6, positioning it as the next crucial step for developers seeking reliable, high-performance AI coding partners. The report highlights a 73.8% success rate on the SWUE benchmark, representing a 5.8% gain over GLM-4.6. Furthermore, GLM-4.7 scored 95.7% on the HLE benchmark, significantly surpassing Gemini 3.0 Pro's 90.5%. The primary architectural innovation is 'preserved thinking,' where the model reuses its internal reasoning blocks across conversations, leading to better long-term memory and stability, which is crucial for complex, multi-turn tasks like debugging or feature development without losing context. This method allows agents to manage complex tasks without relying on constant re-explanation from the user. The model also shows strong external integration, scoring 52.0% on web browsing, and excels at tool usage. The improved performance on reasoning and tool integration, rather than just raw coding metrics, is framed as the key differentiator, ensuring that agents maintain context and logical flow over extended interactions, ultimately offering a significant economic advantage by reducing development time and complexity.

### GLM-4.7 Benchmark Performance

- SWUE score of 73.8% (5.8% gain over 4.6)
- HLE score of 95.7% (beating Gemini 3.0 Pro's 90.5%)
- Coding benchmark score of 84.4%

### Key Architectural Shift

- Introduction of 'preserved thinking' (reusing internal reasoning blocks)
- Enhanced long-term memory and stability
- Reduced information loss across multi-turn conversations

### Agentic Capabilities

- Tool usage (e.g., Python, R, CLI) is now robust
- Web browsing scored 52.0%
- Excels at complex tasks like debugging and deployment

### Philosophical Shift

- Focus moves from raw quantitative performance to qualitative improvements in reasoning, control, and integration
- Success measured by seamless integration into workflows, not just benchmark numbers

### Practical Implications

- Offers significant cost and efficiency savings for developers working on long-horizon projects
- Reduces the need to constantly re-explain context to the AI agent

![Screenshot at 00:13: The introduction screen for the GLM-4.7 research report, setting the stage for a deep dive into the model's capabilities.](https://ss.rapidrecap.app/screens/KvW3K6eQj9s/00-00-13.jpg)
![Screenshot at 00:36: A visual representation of benchmark tables being referenced, highlighting the quantitative improvements of GLM-4.7.](https://ss.rapidrecap.app/screens/KvW3K6eQj9s/00-00-36.jpg)
![Screenshot at 01:18: The speakers point out that GLM-4.7 excels exactly where developers struggle most, solving real-world messy problems.](https://ss.rapidrecap.app/screens/KvW3K6eQj9s/00-01-18.jpg)
![Screenshot at 02:27: The speaker notes that the success of the new model in a shell environment demonstrates its ability to execute complex commands.](https://ss.rapidrecap.app/screens/KvW3K6eQj9s/00-02-27.jpg)
![Screenshot at 04:50: The speaker summarizes the core finding: the raw intelligence boost underpinning the model's improved reasoning.](https://ss.rapidrecap.app/screens/KvW3K6eQj9s/00-04-50.jpg)