---
license: mit
library_name: transformers
---
# MiMo-V2-Flash
**MiMo-V2-Flash** is a Mixture-of-Experts (MoE) language model with **309B total parameters** and **15B active parameters**. Designed for high-speed reasoning and agentic workflows, it utilizes a novel hybrid attention architecture and Multi-Token Prediction (MTP) to achieve state-of-the-art performance while significantly reducing inference costs.
-----
## 1. Introduction
MiMo-V2-Flash creates a new balance between long-context modeling capability and inference efficiency. Key features include:
* **Hybrid Attention Architecture**: Interleaves Sliding Window Attention (SWA) and Global Attention (GA) with a 5:1 ratio and an aggressive 128-token window. This reduces KV-cache storage by nearly 6x while maintaining long-context performance via learnable **attention sink bias**.
* **Multi-Token Prediction (MTP)**: Equipped with a lightweight MTP module (0.33B params/block) using dense FFNs. This triples output speed during inference and will be good to accelerates rollout in RL training.
* **Efficient Pre-Training**: Trained on 27T tokens using FP8 mixed precision and native 32k seq length. The context window supports up to 256k length.
* **Agentic Capabilities**: Post-training utilizes Multi-Teacher On-Policy Distillation (MOPD) and large-scale agentic RL, achieving superior performance on **SWE-Bench** and complex reasoning tasks.
-----
## 2. Model Downloads
| Model | Total Params | Active Params | Context Length | Download |
| :--------------------- | :----------: | :-----------: | :------------: | :-------------------------------------------------------------------: |
| **MiMo-V2-Flash-Base** | 309B | 15B | 256k | [🤗 HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash-Base) |
| **MiMo-V2-Flash** | 309B | 15B | 256k | [🤗 HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash) |
> [!IMPORTANT]
> We also open-source the 3-layer MTP weights to foster community research.
-----
## 3. Evaluation Results
### Base Model Evaluation
MiMo-V2-Flash-Base demonstrates strong performance across standard benchmarks, surpassing models with significantly larger parameter counts.
| Category | Benchmark | Setting/Length | MiMo-V2-Flash Base | Kimi-K2 Base | DeepSeek-V3.1 Base | DeepSeek-V3.2 Exp Base |
| :--------------- | :---------------------- | :------------- | :----------------: | :-------------: | :----------------: | :--------------------: |
| **Params** | **#Activated / #Total** | - | **15B / 309B** | **32B / 1043B** | **37B / 671B** | **37B / 671B** |
| **General** | BBH | 3-shot | 88.5 | 88.7 | 88.2 | 88.7 |
| | MMLU | 5-shot | 86.7 | 87.8 | 87.4 | 87.8 |
| | MMLU-Redux | 5-shot | 90.6 | 90.2 | 90.0 | 90.4 |
| | MMLU-Pro | 5-shot | 73.2 | 69.2 | 58.8 | 62.1 |
| | DROP | 3-shot | 84.7 | 83.6 | 86.3 | 86.6 |
| | ARC-Challenge | 25-shot | 95.9 | 96.2 | 95.6 | 95.5 |
| | HellaSwag | 10-shot | 88.5 | 94.6 | 89.2 | 89.4 |
| | WinoGrande | 5-shot | 83.8 | 85.3 | 85.9 | 85.6 |
| | TriviaQA | 5-shot | 80.3 | 85.1 | 83.5 | 83.9 |
| | GPQA-Diamond | 5-shot | 55.1 | 48.1 | 51.0 | 52.0 |
| | SuperGPQA | 5-shot | 41.1 | 44.7 | 42.3 | 43.6 |
| | SimpleQA | 5-shot | 20.6 | 35.3 | 26.3 | 27.0 |
| **Math** | GSM8K | 8-shot | 92.3 | 92.1 | 91.4 | 91.1 |
| | MATH | 4-shot | 71.0 | 70.2 | 62.6 | 62.5 |
| | AIME 24&25 | 2-shot | 35.3 | 31.6 | 21.6 | 24.8 |
| **Code** | HumanEval+ | 1-shot | 70.7 | 84.8 | 64.6 | 67.7 |
| | MBPP+ | 3-shot | 71.4 | 73.8 | 72.2 | 69.8 |
| | CRUXEval-I | 1-shot | 67.5 | 74.0 | 62.1 | 63.9 |
| | CRUXEval-O | 1-shot | 79.1 | 83.5 | 76.4 | 74.9 |
| | MultiPL-E HumanEval | 0-shot | 59.5 | 60.5 | 45.9 | 45.7 |
| | MultiPL-E MBPP | 0-shot | 56.7 | 58.8 | 52.5 | 50.6 |
| | BigCodeBench | 0-shot | 70.1 | 61.7 | 63.0 | 62.9 |
| | LiveCodeBench v6 | 1-shot | 30.8 | 26.3 | 24.8 | 24.9 |
| | SWE-Bench (AgentLess) | 3-shot | 30.8 | 28.2 | 24.8 | 9.4* |
| **Chinese** | C-Eval | 5-shot | 87.9 | 92.5 | 90.0 | 91.0 |
| | CMMLU | 5-shot | 87.4 | 90.9 | 88.8 | 88.9 |
| | C-SimpleQA | 5-shot | 61.5 | 77.6 | 70.9 | 68.0 |
| **Multilingual** | GlobalMMLU | 5-shot | 76.6 | 80.7 | 81.9 | 82.0 |
| | INCLUDE | 5-shot | 71.4 | 75.3 | 77.2 | 77.2 |
| **Long Context** | NIAH-Multi | 32K | 99.3 | 99.8 | 99.7 | 85.6* |
| | | 64K | 99.9 | 100.0 | 98.6 | 85.9* |
| | | 128K | 98.6 | 99.5 | 97.2 | 94.3* |
| | | 256K | 96.7 | - | - | - |
| | GSM-Infinite Hard | 16K | 37.7 | 34.6 | 41.5 | 50.4 |
| | | 32K | 33.7 | 26.1 | 38.8 | 45.2 |
| | | 64K | 31.5 | 16.0 | 34.7 | 32.6 |
| | | 128K | 29.0 | 8.8 | 28.7 | 25.7 |
> \* indicates the model may fail to follow the prompt or format.
### Post-training Model Evaluation
Following our Post-Training Paradigm with MOPD and Agentic RL, the model achieves SOTA reasoning and agentic performance.
| Benchmark | MiMo-V2 Flash | Kimi-K2 Thinking | DeepSeek-V3.2 Thinking | Gemini-3.0 Pro | Claude Sonnet 4.5 | GPT-5 High |
| :----------------------------- | :-----------: | :--------------: | :--------------------: | :------------: | :---------------: | :--------: |
| **Reasoning** | | | | | | |
| MMLU-Pro | 84.9 | 84.6 | 85.0 | 90.1 | 88.2 | 87.5 |
| GPQA-Diamond | 83.7 | 84.5 | 82.4 | 91.9 | 83.4 | 85.7 |
| HLE (no tools) | 22.1 | 23.9 | 25.1 | 37.5 | 13.7 | 26.3 |
| AIME 2025 | 94.1 | 94.5 | 93.1 | 95.0 | 87.0 | 94.6 |
| HMMT Feb. 2025 | 84.4 | 89.4 | 92.5 | 97.5 | 79.2 | 88.3 |
| LiveCodeBench-v6 | 80.6 | 83.1 | 83.3 | 90.7 | 64.0 | 84.5 |
| **General Writing** | | | | | | |
| Arena-Hard (Hard Prompt) | 54.1 | 71.9 | 53.4 | 72.6 | 63.3 | 71.9 |
| Arena-Hard (Creative Writing) | 86.2 | 80.1 | 88.8 | 93.6 | 76.7 | 92.2 |
| **Long Context** | | | | | | |
| LongBench V2 | 60.6 | 45.1 | 58.4 | 65.6 | 61.8 | - |
| MRCR | 45.7 | 44.2 | 55.5 | 89.7 | 55.4 | - |
| **Code Agent** | | | | | | |
| SWE-Bench Verified | 73.4 | 71.3 | 73.1 | 76.2 | 77.2 | 74.9 |
| SWE-Bench Multilingual | 71.7 | 61.1 | 70.2 | - | 68.0 | 55.3 |
| Terminal-Bench Hard | 30.5 | 30.6 | 35.4 | 39.0 | 33.3 | 30.5 |
| Terminal-Bench 2.0 | 38.5 | 35.7 | 46.4 | 54.2 | 42.8 | 35.2 |
| **General Agent** | | | | | | |
| BrowseComp | 45.4 | - | 51.4 | - | 24.1 | 54.9 |
| BrowseComp (w/ Context Manage) | 58.3 | 60.2 | 67.6 | 59.2 | - | - |
| \\(\tau^2\\)-Bench | 80.3 | 74.3 | 80.3 | 85.4 | 84.7 | 80.2 |
-----
## 4. Model Architecture
### Hybrid Sliding Window Attention
MiMo-V2-Flash addresses the quadratic complexity of long contexts by interleaving Local Sliding Window Attention (SWA) and Global Attention (GA).
* **Configuration**: Stacks of \\(M=8\\) hybrid blocks. Each block contains \\(N=5\\) SWA layers followed by 1 GA layer.
* **Efficiency**: SWA layers use a window size of 128 tokens, reducing KV cache significantly.
* **Sink Bias**: Learnable attention sink bias is applied to maintain performance despite the aggressive window size.
### Lightweight Multi-Token Prediction (MTP)
Unlike traditional speculative decoding, our MTP module is natively integrated for training and inference.
* **Structure**: Uses a dense FFN (instead of MoE) and SWA (instead of GA) to keep the parameter count low (0.33B per block).
* **Performance**: Facilitates self-speculative decoding, tripling generation speed and mitigating GPU idleness during small-batch RL training.
-----
## 5. Post-Training Technical Highlights
MiMo-V2-Flash leverages a post-training pipeline designed to maximize reasoning and agentic capabilities through innovative distillation and reinforcement learning strategies.
### 5.1 Multi-Teacher On-Policy Distillation (MOPD)
We introduce **Multi-Teacher On-Policy Distillation (MOPD)**, a new paradigm that formulates knowledge distillation as a reinforcement learning process.
* **Dense Token-Level Guidance**: Unlike methods relying on sparse sequence-level feedback, MOPD utilizes domain-specific expert models (teachers) to provide supervision at every token position.
* **On-Policy Optimization**: The student model learns from its own generated responses rather than a fixed dataset. This eliminates exposure bias and ensures smaller, more stable gradient updates.
* **Inherent Reward Robustness**: Rewards are derived from the distribution divergence between student and teacher, making the process naturally resistant to reward hacking.
### 5.2 Scaling Agentic RL
We significantly scale up the agentic training environments to improve intelligence and generalization.
* **Massive Code Agent Environments**: We utilize real-world GitHub issues to create over 100,000 verifiable tasks. Our automated pipeline maintains a Kubernetes cluster capable of running over 10,000 concurrent pods with a 70% environment setup success rate.
* **Multimodal Verifier for WebDev**: For web development tasks, we employ a vision-based verifier that evaluates code execution via recorded videos rather than static screenshots. This reduces visual hallucination and ensures functional correctness.
* **Cross-Domain Generalization**: Our experiments show that large-scale RL training on code agents effectively generalizes to other domains, boosting performance in Math and General Agent tasks.
### 5.3 Advanced RL Infrastructure
To support high-throughput RL training for large-scale MoE models, we implemented several infrastructure optimizations on top of SGLang and Megatron-LM.
* **Rollout Routing Replay (R3)**: Addresses numerical precision inconsistencies in MoE routing between inference and training. R3 reuses the exact routed experts from rollout during the training pass, ensuring consistency with negligible overhead.
* **Request-Level Prefix Cache**: In multi-turn agent training, this cache stores KV states and routed experts from prior turns. It avoids re-computation and ensures sampling consistency across turns.
* **Fine-Grained Data Scheduler**: We extend the rollout engine to schedule fine-grained sequences instead of micro-batches. Combined with partial rollout, this significantly reduces GPU idleness caused by long-tail stragglers.
* **Toolbox & Tool Manager**: A two-layer design using Ray actor pools to handle resource contention. It eliminates cold-start delays for tool execution and isolates task logic from system policies.
-----
## 6. Inference & Deployment
MiMo-V2-Flash supports FP8 mixed precision inference. We recommend using **SGLang** for optimal performance.
### Quick Start with SGLang
```bash
pip install sglang
# Launch server
python3 -m sglang.launch_server \
--model-path XiaomiMiMo/MiMo-V2-Flash \
--served-model-name mimo-v2-flash \
--pp-size 1 \
--dp-size 2 \
--enable-dp-attention \
--tp-size 8 \
--moe-a2a-backend deepep \
--page-size 1 \
--host 0.0.0.0 \
--port 9001 \
--trust-remote-code \
--mem-fraction-static 0.75 \
--max-running-requests 128 \
--chunked-prefill-size 16384 \
--reasoning-parser qwen3 \
--tool-call-parser mimo \
--context-length 262144 \
--attention-backend fa3 \
--speculative-algorithm EAGLE \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--enable-mtp
# Send request
curl -i http://localhost:9001/v1/chat/completions \
-H 'Content-Type:application/json' \
-d '{
"messages" : [{
"role": "user",
"content": "Nice to meet you MiMo"
}],
"model": "mimo-v2-flash",
"max_tokens": 4096,
"temperature": 0.8,
"top_p": 0.95,
"stream": true,
"chat_template_kwargs": {
"enable_thinking": true
}
}'
```
### Notifications
#### 1. System prompt
> [!IMPORTANT]
> The following system prompts are **HIGHLY** recommended, please choose from English and Chinese version.
English
```plaintext
You are MiMo, an AI assistant developed by Xiaomi.
Today's date: {date} {week}. Your knowledge cutoff date is December 2024.
```
Chinese
```plaintext
你是MiMo（中文名称也是MiMo），是小米公司研发的AI智能助手。
今天的日期：{date} {week}，你的知识截止日期是2024年12月。
```
#### 2. Sampling parameters
> [!IMPORTANT]
> Recommended sampling parameters:
>
> `top_p=0.95`
>
> `temperature=0.8` for math, writing, web-dev
>
> `temperature=0.3` for agentic taks (e.g., vibe-coding, tool-use)
#### 3. Tool-use practice
> [!IMPORTANT]
> In the thinking mode with multi-turn tool calls, the model returns a `reasoning_content` field alongside `tool_calls`. To continue the conversation, the user must persist all history `reasoning_content` in the `messages` array of each subsequent request.
-----
## 7. Citation
If you find our work helpful, please cite our technical report:
```bibtex
@misc{mimo2025flash,
title={MiMo-V2-Flash Technical Report},
author={LLM-Core Xiaomi},
year={2025},
url={https://github.com/XiaomiMiMo/MiMo-V2-Flash/paper.pdf}
}
```
## 8. Contact
Please contact us at [mimo@xiaomi.com](mailto:mimo@xiaomi.com), join our WeChat group below or open an issue if you have any questions.
