GLM-5.3
Active Parameters
753.9B
Context Length
1.05M
Modality
Text
Architecture
Mixture of Experts (MoE)
License
other
Release Date
25 Aug 2026
Knowledge Cutoff
-
API Pricing (per 1M)
Input: $1.40 · Output: $4.40
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
91x RTX 4090
24GB VRAM
Datacenter
23x NVIDIA A100
80GB VRAM
Apple Silicon
19x Apple M3 Max
128GB VRAM
1,048,576 tokens
Consumer
469x RTX 4090
24GB VRAM
Datacenter
102x NVIDIA A100
80GB VRAM
Apple Silicon
108x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#16
| Benchmark | Score | Rank |
|---|---|---|
Agentic Index | max 0.53 | 6 |
Agentic Coding | 0.61 | 12 |
Web Development | max 1623 | 22 |
Coding Index | max 0.75 | 23 |
General Text | max 1481 | 24 |
Coding | 0.79 | 27 |
General | 0.76 | 28 |
LiveBench Average | 0.76 | 29 |
Agent Arena | max 0.01 | 31 |
Reasoning | 0.86 | 33 |
Mathematics | 0.88 | 41 |
Data Analysis | 0.70 | 48 |
Intelligence Index | max 0.32 low 0.23 | 76 141 |
Rankings
Overall Rank
#16
Coding Rank
#15
About GLM-5.3
GLM-5.3 is a large-scale mixture-of-experts language model developed by Zhipu AI / Z.ai utilizing dynamic sparse attention. It provides state-of-the-art multilingual comprehension, complex mathematical reasoning, and coding capabilities.
Technical Specifications
Attention
Attention Structure
DeepSeek Sparse Attention
Attention Heads
64
Key-Value Heads
64
Attention Head Dimension
192
Position Embedding
ROPE
RoPE Theta
8,000,000
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
6,144
Number of Layers
78
FFN Intermediate Size (Dense)
12,288
Multi-Token Prediction Heads
1
Tokenizer
Vocabulary Size
154,880
Mixture of Experts
Total Expert Parameters
51.6B
Number of Experts
256
Active Experts
8
Shared Experts
1
FFN Intermediate Size (per Expert)
2,048
Dense Layers Before MoE
3
Resources
About GLM Family
General Language Models from Z.ai