Active Parameters
1.02T
Context Length
1.05M
Modality
Text
Architecture
Mixture of Experts (MoE)
License
MIT License
Release Date
21 Sept 2026
Knowledge Cutoff
-
API Pricing (per 1M)
Self-hosted only
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
143x RTX 4090
24GB VRAM
Datacenter
35x NVIDIA A100
80GB VRAM
Apple Silicon
31x Apple M3 Max
128GB VRAM
1,048,576 tokens
Consumer
183x RTX 4090
24GB VRAM
Datacenter
44x NVIDIA A100
80GB VRAM
Apple Silicon
40x Apple M3 Max
128GB VRAM
No evaluation benchmarks for MiMo V2.6 Pro RL available.
Overall Rank
-
Coding Rank
-
MiMo V2.6 Pro RL is Xiaomi's flagship reasoning-oriented model trained using reinforcement learning for complex problem solving and agent workflows. It achieves enhanced performance across coding, mathematics, and complex multi-step tasks.
Attention
Attention Structure
Single-Head Attention
Attention Heads
128
Key-Value Heads
8
Attention Head Dimension
192
Position Embedding
ROPE
RoPE Theta
10,000,000
Sliding Window Attention
Yes
Sliding Window Size
128
Sliding Window Ratio
85.7%
Linear Attention
No
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Auxiliary Parameters
-
Hidden Dimension Size
6,144
Number of Layers
70
FFN Intermediate Size (Dense)
16,384
Multi-Token Prediction Heads
5
Tokenizer
Vocabulary Size
152,576
Mixture of Experts
Total Expert Parameters
42.0B
Number of Experts
384
Active Experts
8
Shared Experts
-
FFN Intermediate Size (per Expert)
2,048
Dense Layers Before MoE
1
MiMo-V2-Flash is a Mixture-of-Experts (MoE) model with hybrid attention architecture designed for high-speed reasoning and agentic workflows. It features Multi-Token Prediction (MTP) to achieve state-of-the-art performance while significantly reducing inference costs. The model is optimized for long-context modeling and efficient inference.
Assistant
Online