GLM 5.2 is my new favorite model...

하이라이트

• GLM 5.2 contains 744 billion total parameters with 40 billion active parameters.

• The model achieved a score of 51 on the artificial intelligence index, outperforming its predecessor by 11 points.

• GLM 5.2 holds the top overall position on the Design Arena single-turn HTML web design leaderboard.

• At an intelligence level comparable to frontier models, GLM 5.2 costs approximately 50 cents per task.

• The model is MIT licensed, enabling open access for developers.

타임라인

1. 00:00 - 01:46

   GLM 5.2 Model Overview and Benchmarks

   This model represents a significant jump in capabilities despite retaining the same parameter size as its predecessor. It leads the current open model landscape by a clear margin across reasoning, coding, and science benchmarks. It successfully competes with major proprietary models in both code generation and complex task evaluation.

   • GLM 5.2 utilizes 744 billion total parameters with 40 billion active parameters.
   • The model reached an intelligence index score of 51, marking an 11-point improvement over version 5.1.
   • Performance on benchmarks like GPT-Val and Deep SWE matches frontier models such as Gemini 3.5 Flash and GPT-5.4.
2. 01:47 - 05:04

   Web Design and UI Performance

   Design capabilities are a primary strength, with the model avoiding common AI aesthetic anti-patterns. While it requires text-based prompts rather than screenshot inputs, it effectively recreates complex UI structures. It consistently produces cleaner, more polished results compared to other models tested in side-by-side comparisons.

   • GLM 5.2 ranked first on the Design Arena single-turn HTML web design leaderboard.
   • The model demonstrates proficiency with libraries including Chart.js, Three.js, and Tailwind CSS.
   • Recreation tests show the model produces high-quality UI results despite lacking direct image input capabilities.
3. 05:05 - 10:23

   Application Development and Coding Capabilities

   Tests reveal that the model handles complex coding tasks, such as creating finance dashboards, with minimal errors in a single prompt. It effectively selects common development stacks, though user intervention may be required to specify exact library preferences. While some minor logic inversions occurred in game development tasks, the overall output remained highly functional compared to competitors.

   • The model successfully generates functional 3D applications and full-stack web dashboards.
   • Development tasks involving Next.js and Prisma demonstrate the model's ability to handle integrated backend and frontend logic.
   • Coding performance matches or exceeds that of other specialized coding models in single-prompt scenarios.
4. 10:24 - 12:40

   Efficiency, Cost, and Future Outlook

   Data indicates that GLM 5.2 provides a high intelligence-to-cost ratio. While it is more token-hungry than some peers, its overall performance warrants its use as a replacement for proprietary models in many scenarios. The availability of this model under an MIT license provides a significant resource for those seeking high-tier performance without reliance on restricted frontier providers.

   • Operating costs average 50 cents per task, making it the most cost-efficient model at its intelligence tier.
   • Token consumption is higher than some smaller models, averaging 43,000 tokens per task.
   • The model maintains competitive speeds compared to other open-source models near its performance level.