GLM-4.7

📖 Check out the GLM-4.7 technical blog, technical report(GLM-4.5). 📍 Use GLM-4.7 API services on Z.ai API Platform. 👉 One click to GLM-4.7.

Introduction

GLM-4.7, your new coding partner, is coming with the following features:

• Core Coding: GLM-4.7 brings clear gains, compared to its predecessor GLM-4.6, in multilingual agentic coding and terminal-based tasks, including (73.8%, +5.8%) on SWE-bench, (66.7%, +12.9%) on SWE-bench Multilingual, and (41%, +16.5%) on Terminal Bench 2.0. GLM-4.7 also supports thinking before acting, with significant improvements on complex tasks in mainstream agent frameworks such as Claude Code, Kilo Code, Cline, and Roo Code.
• Vibe Coding: GLM-4.7 takes a big step forward in improving UI quality. It produces cleaner, more modern webpages and generates better-looking slides with more accurate layout and sizing.
• Tool Using: GLM-4.7 achieves significantly improvements in Tool using. Significant better performances can be seen on benchmarks such as τ^2-Bench and on web browsing via BrowseComp.
• Complex Reasoning: GLM-4.7 delivers a substantial boost in mathematical and reasoning capabilities, achieving (42.8%, +12.4%) on the HLE (Humanity’s Last Exam) benchmark compared to GLM-4.6.

You can also see significant improvements in many other scenarios such as chat, creative writing, and role-play scenario.

Performances on Benchmarks. More detailed comparisons of GLM-4.7 with other models GPT-5-High, GPT-5.1-High, Claude Sonnet 4.5, Gemini 3.0 Pro, DeepSeek-V3.2, Kimi K2 Thinking, on 17 benchmarks (including 8 reasoning, 5 coding, and 3 agents benchmarks) can be seen in the below table.

Coding: AGI is a long journey, and benchmarks are only one way to evaluate performance. While the metrics provide necessary checkpoints, the most important thing is still how it feels. True intelligence isn't just about acing a test or processing data faster; ultimately, the success of AGI will be measured by how seamlessly it integrates into our lives-"coding" this time.

Getting started with GLM-4.7

Interleaved Thinking & Preserved Thinking

GLM-4.7 further enhances Interleaved Thinking (a feature introduced since GLM-4.5) and introduces Preserved Thinking and Turn-level Thinking. By thinking between actions and staying consistent across turns, it makes complex tasks more stable and more controllable:

• Interleaved Thinking: The model thinks before every response and tool calling, improving instruction following and the quality of generation.
• Preserved Thinking: In coding agent scenarios, the model automatically retains all thinking blocks across multi-turn conversations, reusing the existing reasoning instead of re-deriving from scratch. This reduces information loss and inconsistencies, and is well-suited for long-horizon, complex tasks.
• Turn-level Thinking: The model supports per-turn control over reasoning within a session—disable thinking for lightweight requests to reduc