MiMo V2.6 Flash RL
Active Parameters
309B
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
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
36x RTX 4090
24GB VRAM
Datacenter
10x NVIDIA A100
80GB VRAM
Apple Silicon
7x Apple M3 Max
128GB VRAM
1,048,576 tokens
Consumer
46x RTX 4090
24GB VRAM
Datacenter
12x NVIDIA A100
80GB VRAM
Apple Silicon
10x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for MiMo V2.6 Flash RL available.
Rankings
Overall Rank
-
Coding Rank
-
About MiMo V2.6 Flash RL
MiMo V2.6 Flash RL is a high-efficiency reasoning model developed by Xiaomi, post-trained with reinforcement learning for accelerated reasoning and tool-use capabilities. It provides rapid response generation suitable for real-time mobile and edge assistant scenarios.
Technical Specifications
Attention
Attention Structure
Single-Head Attention
Attention Heads
64
Key-Value Heads
4
Attention Head Dimension
192
Position Embedding
ROPE
RoPE Theta
10,000,000
Sliding Window Attention
Yes
Sliding Window Size
128
Sliding Window Ratio
81.3%
Linear Attention
No
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Auxiliary Parameters
-
Hidden Dimension Size
4,096
Number of Layers
48
FFN Intermediate Size (Dense)
16,384
Multi-Token Prediction Heads
3
Tokenizer
Vocabulary Size
152,576
Mixture of Experts
Total Expert Parameters
15.0B
Number of Experts
256
Active Experts
8
Shared Experts
-
FFN Intermediate Size (per Expert)
2,048
Dense Layers Before MoE
1
Resources
About MiMo V2
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.