ApX logoApX logo

GLM-4.6

Active Parameters

357B

Context Length

200K

Modality

Text

Architecture

Mixture of Experts (MoE)

License

MIT

Release Date

30 Sept 2025

Knowledge Cutoff

-

System Requirements

VRAM requirements for different quantization methods and context sizes

1,024 tokens

751.61 GB VRAM

Consumer

42x RTX 4090

24GB VRAM

Datacenter

11x NVIDIA A100

80GB VRAM

Apple Silicon

9x Apple M3 Max

128GB VRAM

200,000 tokens

830.33 GB VRAM

Consumer

47x RTX 4090

24GB VRAM

Datacenter

13x NVIDIA A100

80GB VRAM

Apple Silicon

10x Apple M3 Max

128GB VRAM

Architecture Diagram

Input TokensToken EmbeddingPosition: AbsoluteHidden: 5.1k · Context: 200K · Vocab: 151.6kx 92 layersRMSNormPre-AttentionMulti-Head Attention96Q / 8KV headsHead dim: 128+RMSNormPre-FFNSparse MoE FFN (8/160 experts)SwishIntermediate: 1.5k+Final RMSNormOutput Logits

Evaluation Benchmarks

Rank

#92

BenchmarkScoreRank

Graduate-Level QA

GPQA

0.81

22

Professional Knowledge

MMLU Pro

0.82

31

Web Development

WebDev Arena

1340

70

General Text

Text Arena

1424

77

Rankings

Overall Rank

#92

Coding Rank

#71

About GLM-4.6

GLM-4.6 is a large language model developed by Z.ai, designed to facilitate advanced applications in artificial intelligence. This model is engineered to operate efficiently across a spectrum of complex tasks, including sophisticated coding, extended context processing, and agentic operations. Its bilingual capabilities, supporting both English and Chinese, extend its applicability across diverse linguistic contexts. The model’s purpose is to serve as a robust foundation for building intelligent systems capable of nuanced reasoning and autonomous interaction.

Architecturally, GLM-4.6 implements a Mixture-of-Experts (MoE) configuration, incorporating 357 billion total parameters, with 32 billion parameters actively utilized during a given forward pass. The model's design features a context window expanded to 200,000 tokens, enabling it to process and maintain coherence over substantial input sequences. Innovations within its attention mechanism include Grouped-Query Attention (GQA) with 96 attention heads, and the integration of a partial Rotary Position Embedding (RoPE) for positional encoding. Normalization is managed through QK-Norm, contributing to stabilized attention logits. These architectural choices aim to balance computational efficiency with enhanced performance in complex cognitive operations.

The operational characteristics of GLM-4.6 are optimized for real-world development workflows. It demonstrates superior coding performance, leading to more visually polished front-end generation and improved real-world application results. The model exhibits enhanced reasoning capabilities, which are further augmented by its integrated tool-use functionality during inference. This facilitates the creation of more capable agents proficient in search-based tasks and role-playing scenarios. Furthermore, GLM-4.6 achieves improved token efficiency, completing tasks with approximately 15% fewer tokens compared to its predecessor, GLM-4.5, thereby offering a more cost-effective inference profile.

Technical Specifications

Attention

Attention Structure

Multi-Head Attention

Attention Heads

96

Key-Value Heads

8

Attention Head Dimension

128

Position Embedding

Absolute Position Embedding

RoPE Theta

1,000,000

Sliding Window Attention

No

Sliding Window Size

-

Sliding Window Ratio

-

Linear Attention

-

Linear Attention Ratio

-

Normalization

RMS Normalization

Activation Function

Swish

Dimensions

Hidden Dimension Size

5,120

Number of Layers

92

FFN Intermediate Size (Dense)

1,536

Multi-Token Prediction Heads

1

Tokenizer

Vocabulary Size

151,552

Mixture of Experts

Total Expert Parameters

32.0B

Number of Experts

160

Active Experts

8

Shared Experts

1

FFN Intermediate Size (per Expert)

1,536

Dense Layers Before MoE

3

About GLM-4

GLM-4 is a series of bilingual (English and Chinese) language models developed by Zhipu AI. The models feature extended context windows, superior coding performance, advanced reasoning capabilities, and strong agent functionalities. GLM-4.6 offers improvements in tool use and search-based agents.


Other GLM-4 Models