---
source: 'https://howaiworks.ai/blog/glm-4-7-flash-announcement'
section: blog
title: 'GLM-4.7-Flash: King of MoE Models in 30B Class'
description: >-
  Z.ai releases GLM-4.7-Flash, a 30B MoE model with exceptional reasoning,
  coding, and agentic capabilities, rivaling much larger models.
date: '2026-01-21'
author: HowAIWorks Team
tags:
  - GLM-4.7-Flash
  - LLM
  - MoE Architecture
  - AI Agents
  - Large Language Models
  - Z.ai
  - Open Source AI
readingTime: 4 minutes
isReviewed: false
---

# GLM-4.7-Flash: King of MoE Models in 30B Class

> Z.ai releases GLM-4.7-Flash, a 30B MoE model with exceptional reasoning, coding, and agentic capabilities, rivaling much larger models.

## Introduction

The landscape of efficient, high-performance language models has a new contender: **GLM-4.7-Flash**. Developed by the Z.ai team, this model represents a significant leap forward in the 30B parameter class, utilizing a sophisticated **Mixture of Experts (MoE)** architecture to deliver reasoning and coding capabilities that were previously reserved for much larger models.

GLM-4.7-Flash is specifically designed for what Z.ai calls "ARC" tasks—**Agentic, Reasoning, and Coding**. By balancing efficiency with high-level cognitive performance, it aims to become the go-to choice for developers building complex AI agents and sophisticated software engineering tools.

## Technical Architecture & Features

At its core, GLM-4.7-Flash is a **30B-A3B MoE model**. This architecture allows the model to activate only a subset of its parameters during inference, maintaining the speed of a smaller model while benefiting from the knowledge capacity of a larger one.

### Key Technical Specifications

- **131,072 Token Context Window**: Enables the model to process massive amounts of information in a single pass, which is critical for analyzing large codebases or long legal documents.
- **Preserved Thinking Mode**: A unique feature designed to enhance performance in multi-turn agentic tasks. It allows the model to maintain context and "train of thought" more effectively across complex interactions.
- **Multilingual Support**: While highly optimized for English and Chinese, the model shows strong performance across multiple languages.

## Performance Benchmarks

GLM-4.7-Flash doesn't just promise performance; it delivers on some of the industry's most rigorous benchmarks. Most notably, it has set a new standard for models in the 30B class on developer-centric tasks.

| Benchmark              | Score | Significance                                                                  |
| ---------------------- | ----- | ----------------------------------------------------------------------------- |
| **SWE-bench Verified** | 59.2  | Outperforms Qwen3-30B and rivals larger models in software engineering tasks. |
| **AIME 25**            | 91.6  | Competitive with high-end open-source models like GPT-OSS-20B.                |
| **GPQA**               | 75.2  | Demonstrates strong expert-level reasoning capabilities.                      |
| **τ²-Bench**           | 79.5  | High performance in long-context and complex reasoning scenarios.             |
| **LCB v6**             | 64.0  | Strong results in competitive coding benchmarks.                              |

## Why It Matters for Developers

The release of GLM-4.7-Flash is particularly significant for the agentic AI ecosystem. Its combination of a large context window and the **Preserved Thinking mode** makes it an ideal candidate for:

- **AI Software Engineers**: Its high SWE-bench score indicates a superior ability to understand and fix real-world software issues.
- **Autonomous Agents**: The model's focus on agentic tasks ensures it can handle multi-step planning and tool usage effectively.
- **Efficient Deployment**: Thanks to the MoE architecture, it provides a high-performance alternative that is cheaper and faster to run than monolithic 70B+ models.

## How to Get Started

GLM-4.7-Flash is accessible through several channels:

1. **Z.ai API Platform**: Developers can integrate the model into their applications via the official API.
2. **Chat Demo**: A web-based experience is available at [chat.z.ai](https://chat.z.ai) for testing its capabilities.
3. **Local Implementation**: The model is supported by the `transformers` library, allowing for local hosting and fine-tuning using the weights available on Hugging Face.

## Conclusion

GLM-4.7-Flash marks a turning point for "medium-sized" models. By focusing on the essential pillars of modern AI—reasoning, coding, and agentic behavior—Z.ai has produced a model that punches far above its weight class. As the demand for efficient, capable agents grows, models like GLM-4.7-Flash will be at the forefront of the next generation of AI-powered applications.

## Sources

- [GLM-4.7-Flash on Hugging Face](https://huggingface.co/zai-org/GLM-4.7-Flash)
- [Official Z.ai Announcement and Documentation](https://z.ai)

## Frequently Asked Questions

### What is GLM-4.7-Flash?

GLM-4.7-Flash is a large language model based on the Mixture of Experts (MoE) architecture with a 30B-A3B parameter count, developed by Z.ai for reasoning, coding, and agentic tasks.

### How does GLM-4.7-Flash compare to other models?

It is claimed to be the strongest model in the 30B class, outperforming models like Qwen3-30B on benchmarks like SWE-bench and showing competitive performance with larger OSS models.

### What is the context window for GLM-4.7-Flash?

The model supports a context window of up to 131,072 tokens, making it suitable for long-document analysis and complex agentic workflows.

---

Source: https://howaiworks.ai/blog/glm-4-7-flash-announcement — HowAIWorks.ai