glm-4.7

GLM-4.7 is the flagship text language model launched by Zhipu AI, primarily positioned as an all-around AI collaborator tailored for agentic coding and complex agent-based tasks, delivering comprehensive leadership in coding, reasoning, tool collaboration, and immersive creation.

Key Upgrades: Compared to GLM-4.6, its SWE-bench Verified score improved by 5.8% to 73.8%, Terminal Bench 2.0 saw a 16.5% improvement, and HLE reasoning capability surged by 41%.

Applicable Scenarios: Complex demo development, front-end aesthetic generation, deep research, multi-round collaborative decision-making, IP character storytelling, and automated office content creation.

Product Value: It provides an end-to-end deliverable framework of runnable code, reducing manual assembly and repeated debugging, significantly boosting prototype development efficiency.

Competitor Comparison: LiveCodeBench V6 scored 84.9, ranking first among open-source models and surpassing Claude Sonnet 4.5; τ²-Bench tool invocation scored 84.7, reaching the open-source SOTA level.

Real-World Performance: In testing across 100 real-world programming tasks, it demonstrated markedly superior stability and deliverability compared to previous generations, capable of independently completing highly interactive mini-games such as "Plants vs. Zombies."

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🧠 Deep Thinking Engine: Supports interleaved, persistent, and round-based thinking, enabling more stable decomposition of complex tasks at lower costs.

💻 Agentic Coding Expert: Starting from requirement descriptions, it autonomously integrates front-end and back-end components and peripheral device calls, outputting fully runnable code.

🎨 Front-End Aesthetic Upgrade: The PPT 16:9 adaptation rate jumped from 52% to 91%, with UI layouts, color schemes, and components exhibiting greater design sophistication.

🛠️ Superior Tool Collaboration: Function Call + MCP enables flexible integration with external tools; BrowseComp scored 67 points, and τ²-Bench scored 84.7 points.

📊 Strong Dual Capabilities in Reasoning and Coding: HLE reasoning reached 42.8%, SWE-bench multilingual performance hit 66.7%, and its coding capabilities consistently rank first among open-source models.

✍️ Immersive Creative Power: By meticulously crafting sensory details, it builds rich atmospheres, maintains stable character designs, and ensures natural, tension-filled plot progression.