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Figure 34: GLM-4.5 355B · diagram by Sebastian Raschka · click to zoom
Sparse MoEZ.ai 🇨🇳 · July 28, 2025

GLM-4.5 (355B)

Agent-oriented instruction/reasoning hybrid that borrows DeepSeek's dense-prefix MoE layout.

Key detail

Starts with three dense layers before MoE routing, keeps a shared expert, and uses MTP during training.

Total params

355B

Active / token

32B

Context

128K

Fact sheet

Scale
355B total, 32B active (9% active)
Context window
128,000 tokens
Decoder type
Sparse MoE
Attention
GQA with QK-Norm
Layer mix
92 GQA
KV cache / token (bf16)
368 KiB · Very high
License
MIT License
Released

Efficiency

Parameters active per token

9.0%

32B of 355B weights run for each token.

KV cache per token (bf16)

368 KiB

Smaller than 6% of models in the atlas. How it’s calculated

AA Intelligence Index v4.3.2

Source

12.8

Scientific
6.5

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Similar architectures

  • Architecture diagram of GLM-4.7 (355B)

    Z.ai, released 2025-12-22. Sparse MoE decoder, 355B total, 32B active, 198K context. Attention: GQA with QK-Norm.

  • Architecture diagram of MiniMax M2 (230B)

    MiniMax, released 2025-10-23. Sparse MoE decoder, 230B total, 10B active, 192K context. Attention: GQA with QK-Norm and partial RoPE.

  • Architecture diagram of MiniMax-M2.5 (230B)

    MiniMax, released 2026-02-12. Sparse MoE decoder, 230B total, 10B active, 192K context. Attention: GQA with QK-Norm.

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