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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

700.09 GB VRAM

Consumer

39x RTX 4090

24GB VRAM

Datacenter

11x NVIDIA A100

80GB VRAM

Apple Silicon

8x Apple M3 Max

128GB VRAM

200,000 tokens

773.41 GB VRAM

Consumer

43x RTX 4090

24GB VRAM

Datacenter

12x NVIDIA A100

80GB VRAM

Apple Silicon

9x 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

#105

BenchmarkScoreRank

Software Engineering

SWE-bench Verified

0.68

19

Graduate-Level QA

GPQA

0.81

62

Agentic Index

Artificial Analysis

0.19

87

Web Development

WebDev Arena

1339

94

General Text

Text Arena

1424

94

auto

0.46

99

Intelligence Index

Artificial Analysis

0.15

192

Rankings

Overall Rank

#105

Coding Rank

#81

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

Auxiliary Parameters

-

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