How GLM-5.1 Tops Open‑Source Benchmarks and Generates Articles and Short Videos with a Single Prompt

Benchmark Performance

On three representative code evaluation suites—SWE‑Bench Pro, Terminal‑Bench 2.0, and NL2Repo—GLM‑5.1 achieves the highest average score, ranking third globally, first among Chinese models, and first among open‑source models.

Long‑term Task Context

METR research reports that AI’s success rate on programming tasks (~50%) is increasing exponentially, doubling every 4–6 months, making single‑turn QA insufficient and positioning sustained, complex tasks as the new intelligence benchmark.

Design for Extended Scenarios

GLM‑5.1 is engineered for long‑range tasks, showing significant gains in long‑span reasoning, deep chain‑dependency handling, multi‑tool coordination, continuous execution, and goal retention.

Under the same METR evaluation criteria, GLM‑5.1 is the only Chinese model capable of eight‑hour continuous operation and, together with Claude Opus 4.6, one of the few global models with this endurance.

Eight‑Hour Demonstration

The official video shows an 8‑hour run of more than 1,700 steps that produces a fully functional Linux desktop system—including window manager, status bar, applications, VPN manager, Chinese font support, and a game library—packaged in a 4.8 MB file, equivalent to a four‑person team’s one‑week effort.

One‑Sentence Video Generation Example

GLM‑5.1 is available to Coding Plan users. The model was switched in ~/.claude/settings.json to “GLM‑5.1”, then Claude Code was launched in auto‑drive mode with the following prompt (truncated for brevity):

❯ /ralph-wiggum:ralph-loop "抓取文章 https://mp.weixin.qq.com/s/r9s63xZgXhwGCx0g_O0zmA。生成一支抖音短视频，规格与要求如下：
【基础规格】画面比例：9:16（竖屏） 视频时长：60秒 整体风格：科技感、未来感、动感吸引人
【内容要求】提炼文章核心要点（3~5个），形成连贯解说脚本，每段配对应画面描述
【视觉风格】科技风格元素：粒子流光动效、极简线框HUD风 字体：压缩等宽科技字体（如Orbitron/ZCOOL）
【声音设计】背景音乐：EDM电子感强节奏 旁白语速：偏快，语气自信专业
【输出格式】分阶段输出过程进度 最终输出可播放mp4文件。" --max-iterations 10 --completion-promise "DONE"

After about 16 minutes of uninterrupted execution, the model produced a video without stalls, demonstrating higher stability on complex tasks compared with GLM‑5 and a qualitative improvement in aesthetic quality.

Production‑Level Usage

In high‑intensity production development, GLM‑5.1 was used for deep project‑context understanding, code‑logic analysis, and strict coding‑standard enforcement. The model showed marked improvements in autonomous decision‑making, runtime stability, and multi‑tool orchestration.

When handling complex long‑term tasks, GLM‑5.1 can independently plan, write, iterate, and debug code; it can locate exceptions, perform autonomous debugging, and complete fault recovery without human supervision, comparable to an experienced senior engineer.

Model repository: https://huggingface.co/zai-org/GLM-5.1

Blog post: https://z.ai/blog/glm-5.1