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# 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.