zai-org/GLM-4.5

zai-org/GLM-4.5

Description: GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

Language: Python

License: Apache-2.0

Stars: 4353

Forks: 459

Open issues: 27

Created: 2025-07-20T09:24:09Z

Pushed: 2026-02-01T08:28:10Z

Default branch: main

Fork: no

Archived: no

README:

GLM-4.7 & GLM-4.6 & GLM-4.5

[中文阅读](./README_zh.md) | [日本語版](./README_ja.md)

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📖 Check out the GLM-4.7 technical blog, technical report(GLM-4.5), and Zhipu AI technical documentation.

📍 Use GLM-4.7 API services on Z.ai API Platform.

👉 One click to GLM-4.7.

Model Introduction

GLM-4.7

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.

More general, one would also witness significant improvements in many other scenarios such as chat, creative writing, and role-play scenario.


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 reduce latency/cost, enable it for complex tasks to improve accuracy and stability.

More details: https://docs.z.ai/guides/capabilities/thinking-mode

We also provide the lightweight 30B-A3B model GLM-4.7-Flash, offering a new option for lightweight deployment that balances performance and efficiency.

GLM-4.6

Compared with GLM-4.5, GLM-4.6 brings several key improvements:

• Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.
• Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.
• Advanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.
• More capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks.
• Refined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.

We evaluated GLM-4.6 across eight public benchmarks covering agents, reasoning, and coding. Results show clear gains over GLM-4.5, with GLM-4.6 also holding competitive advantages over leading domestic and international models such as DeepSeek-V3.1-Terminus and Claude Sonnet 4.

GLM-4.5

The GLM-4.5 series models are foundation models designed for intelligent agents. GLM-4.5 has 355 billion total parameters with 32 billion active parameters, while GLM-4.5-Air adopts a more compact design with 106 billion total parameters and 12 billion active parameters. GLM-4.5 models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.

Both GLM-4.5 and GLM-4.5-Air are hybrid reasoning models that provide two modes: thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses.

We have open-sourced the base models, hybrid reasoning models, and FP8 versions of the hybrid reasoning models for both GLM-4.5 and GLM-4.5-Air. They are released under the MIT open-source license and can be used commercially and for secondary development.

As demonstrated in our comprehensive evaluation across 12 industry-standard benchmarks, GLM-4.5 achieves exceptional performance with a score of 63.2, in the 3rd place among all the proprietary and open-source models. Notably, GLM-4.5-Air delivers competitive results at 59.8 while maintaining superior efficiency.

For more eval results, show cases, and technical details, please visit our technical report.

Model Downloads

| Model | Download Links | Model Size | Precision | |------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|------------|-----------| | GLM-4.7 | 🤗 Hugging Face 🤖 ModelScope | 355B-A32B | BF16 | | GLM-4.7-FP8 | 🤗 Hugging Face 🤖 ModelScope | 355B-A32B | FP8 | | GLM-4.7-Flash | 🤗 Hugging Face [🤖…