# GLM-4.6

> Z.ai's open-weight coding flagship: 357B-param MoE (32B active), 200K context, MIT-licensed.

- Maker: Z.ai (Zhipu / GLM)
- Line: GLM (flagship)
- Canonical page: https://ai-tldr.dev/models/glm-4-6/
- Updated: 2026-06-18

## In short

GLM-4.6 is the September 30, 2025 flagship release from Z.ai (Zhipu AI's GLM line). It is an open-weight, MIT-licensed Mixture-of-Experts model with 357 billion total parameters and about 32 billion active per token. It has a 200K-token context window and costs $0.60 per million input tokens and $2.20 per million output tokens. You can use it through Z.ai and OpenRouter.

## Facts

| Fact | Value | Source |
| --- | --- | --- |
| Context window | 200K (max output 128K) | [1] |
| Input price | $0.60 / 1M tokens (cached $0.11 / 1M tokens) | [2] |
| Output price | $2.20 / 1M tokens | [2] |
| Licence | MIT · open weights | [1] |
| Released | 30 September 2025 | [3] |
| Parameters | 357B total / 32B active (MoE) | [1] |
| Knowledge cutoff | March 2025 | [1] |

## Sources

1. [zai-org/GLM-4.6 — Hugging Face model card](https://huggingface.co/zai-org/GLM-4.6)
2. [Z.ai API pricing](https://docs.z.ai/guides/overview/pricing)
3. [GLM-4.6 — announcement blog](https://z.ai/blog/glm-4.6)

## Overview

GLM-4.6 is the September 30, 2025 flagship release from Z.ai (Zhipu AI's GLM line). It is an open-weight, MIT-licensed Mixture-of-Experts model with 357 billion total parameters and about 32 billion active per token. Its headline change over GLM-4.5 is a context window expanded from 128K to 200K tokens, which gives it more room for long agentic coding sessions and large-codebase work.

Z.ai positions GLM-4.6 primarily as a coding and agentic model. It ships with an optional thinking mode for extended reasoning and tool use, and Z.ai reports it is roughly 30% more token-efficient than GLM-4.5 on equivalent tasks. The weights are published on Hugging Face (zai-org/GLM-4.6), and the model is wired into popular coding agents such as Claude Code, Cline, Roo Code and Kilo Code.

Across public benchmarks GLM-4.6 posts strong reasoning and coding scores (for example 93.9% on AIME 2025 and 82.8% on LiveCodeBench v6) and, in Z.ai's CC-Bench human-evaluation tests, reaches a 48.6% win rate against Claude Sonnet 4. It sits as one of the stronger open-weight coding models of its generation, though Z.ai notes it still trails the very top closed coding models on some tasks.

## Benchmarks

- AIME 2025: 93.9% ([source](https://llm-stats.com/blog/research/glm-4-6-launch))
- LiveCodeBench v6: 82.8% ([source](https://llm-stats.com/blog/research/glm-4-6-launch))
- BrowseComp: 45.1% ([source](https://llm-stats.com/blog/research/glm-4-6-launch))
- Terminal-Bench: 40.5% ([source](https://llm-stats.com/blog/research/glm-4-6-launch))
- CC-Bench win rate vs Claude Sonnet 4: 48.6% ([source](https://intuitionlabs.ai/articles/glm-4-6-open-source-coding-model))

## Pricing

- Input: $0.60 / 1M tokens per 1M tokens
- Cached input: $0.11 / 1M tokens per 1M tokens
- Output: $2.20 / 1M tokens per 1M tokens

Official Z.ai first-party API pricing. Open weights are also free to self-host under the MIT license.

Source: https://docs.z.ai/guides/overview/pricing

## Strengths

- Open weights under a permissive MIT license — free to self-host, fine-tune and deploy commercially
- 200K-token context window, expanded from 128K in GLM-4.5, for long agentic coding and large-codebase tasks
- Strong real-world coding and agentic performance; integrates with Claude Code, Cline, Roo Code and Kilo Code
- About 30% more token-efficient than GLM-4.5 on equivalent tasks, lowering inference cost
- Optional thinking mode for extended step-by-step reasoning and tool use
- Competitive low API pricing ($0.60 input / $2.20 output per 1M tokens)

## Best for

- Agentic coding inside tools like Claude Code, Cline, Roo Code and Kilo Code
- Long-context tasks: reasoning over large codebases or document sets up to 200K tokens
- Self-hosted or private deployments where open weights and an MIT license are required
- Front-end and full-stack code generation and refactoring
- Math and reasoning workloads (AIME-style problems, competitive coding)
- Cost-sensitive production workloads needing a capable open model at low per-token price

## How to access

- [Z.ai](https://docs.z.ai/guides/llm/glm-4.6) — model id `glm-4.6`
- [OpenRouter](https://openrouter.ai/z-ai/glm-4.6) — model id `z-ai/glm-4.6`

## GLM (flagship) versions

- [GLM-5.2](https://ai-tldr.dev/models/glm-5-2/) (13 June 2026)
- [GLM-5.1](https://ai-tldr.dev/models/glm-5-1/) (7 April 2026)
- [GLM-5](https://ai-tldr.dev/models/glm-5/) (11 February 2026)
- [GLM-4.7](https://ai-tldr.dev/models/glm-4-7/) (22 December 2025)
- GLM-4.6 (30 September 2025) — this page
- [GLM-4.5](https://ai-tldr.dev/models/glm-4-5/) (28 July 2025)

## FAQ

### Is GLM-4.6 open source?

GLM-4.6 ships with open weights under the permissive MIT license. They are published on Hugging Face (zai-org/GLM-4.6), so you can self-host, fine-tune and deploy the model commercially for free; you only pay if you use Z.ai's hosted API.

### What is GLM-4.6's context window?

GLM-4.6 has a 200K-token context window, expanded from 128K in GLM-4.5, with a maximum output of 128K tokens per response.

### How big is GLM-4.6 and what architecture does it use?

It is a Mixture-of-Experts (MoE) model with 357 billion total parameters, of which roughly 32 billion are active per token. It also supports an optional thinking mode for extended reasoning and tool use.

### How much does GLM-4.6 cost to use?

On Z.ai's first-party API, GLM-4.6 costs $0.60 per million input tokens and $2.20 per million output tokens, with cached input at $0.11 per million. Because the weights are open under MIT, self-hosting is also an option.

## Links

- [GLM-4.6 — official Z.ai docs](https://docs.z.ai/guides/llm/glm-4.6)
- [GLM-4.6 — announcement blog](https://z.ai/blog/glm-4.6)
- [zai-org/GLM-4.6 — Hugging Face model card](https://huggingface.co/zai-org/GLM-4.6)
- [GLM-4.5/4.6/4.7 — GitHub repository](https://github.com/zai-org/GLM-4.5)
- [Z.ai API pricing](https://docs.z.ai/guides/overview/pricing)
- [GLM-4.6 on OpenRouter](https://openrouter.ai/z-ai/glm-4.6)
- [GLM-4.6 launch benchmarks — LLM Stats](https://llm-stats.com/blog/research/glm-4-6-launch)
- [GLM-4.6 vs Sonnet & GPT-5 review — IntuitionLabs](https://intuitionlabs.ai/articles/glm-4-6-open-source-coding-model)

Source: AI/TLDR LLM registry, https://ai-tldr.dev/models/glm-4-6/