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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

-

API Pricing (per 1M)

Input: $0.55 · Output: $2.20

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

#89

BenchmarkScoreRank

Software Engineering

SWE-bench Verified

0.68

17

Graduate-Level QA

GPQA

0.81

49

General Text

Text Arena

1425

72

Web Development

WebDev Arena

1341

75

Agentic Index

Artificial Analysis

0.19

76

0.46

87

Intelligence Index

Artificial Analysis
auto

0.18

Standard

0.15

140

161

Rankings

Overall Rank

#89

Coding Rank

#68

About GLM-4.6

GLM-4.6 is a 357B Mixture-of-Experts model developed by Z.ai activating 32B parameters for advanced coding, UI generation, and agentic search. It delivers enhanced token efficiency and bilingual reasoning across a 200K token context window.

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
GLM-4.6: Specifications and GPU VRAM Requirements