MiniMax M2
Active Parameters
229B
Context Length
128K
Modality
Text
Architecture
Mixture of Experts (MoE)
License
MIT
Release Date
7 Nov 2025
Knowledge Cutoff
Jun 2024
API Pricing (per 1M)
Input: $0.30 · Output: $1.20
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
26x RTX 4090
24GB VRAM
Datacenter
7x NVIDIA A100
80GB VRAM
Apple Silicon
5x Apple M3 Max
128GB VRAM
128,000 tokens
Consumer
27x RTX 4090
24GB VRAM
Datacenter
8x NVIDIA A100
80GB VRAM
Apple Silicon
6x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#101
| Benchmark | Score | Rank |
|---|---|---|
Professional Knowledge | 0.82 | 22 |
Software Engineering | 0.61 | 26 |
Graduate-Level QA | 0.78 | 70 |
Web Development | 1298 | 89 |
General Text | 1346 | 121 |
Intelligence Index | 0.19 | 139 |
Rankings
Overall Rank
#101
Coding Rank
#96
About MiniMax M2
MiniMax M2 is a 229B Mixture-of-Experts transformer activating 10B parameters for high-throughput coding and autonomous agent workflows. It features structured reasoning traces and a 128K context window for continuous CI/CD integration.
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
32
Key-Value Heads
8
Attention Head Dimension
128
Position Embedding
Absolute Position Embedding
RoPE Theta
5,000,000
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
4,096
Number of Layers
32
FFN Intermediate Size (Dense)
1,536
Multi-Token Prediction Heads
3
Tokenizer
Vocabulary Size
200,064
Mixture of Experts
Total Expert Parameters
10.0B
Number of Experts
8
Active Experts
2
Shared Experts
-
FFN Intermediate Size (per Expert)
1,536
Dense Layers Before MoE
-
About MiniMax M2
MiniMax's efficient MoE models built for coding and agentic workflows.
Other MiniMax M2 Models
- No related models available