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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
MiMo-V2-Flash: Efficient Reasoning, Coding, and Agentic Foundation Model
| Date | Stars |
|---|---|
| 2026-07-31 | 1363 |
| 2026-08-06 | 1363 |
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<a href="https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash" target="_blank">🤗 HuggingFace</a>
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<a href="https://github.com/XiaomiMiMo/MiMo-V2-Flash/blob/main/paper.pdf" target="_blank">📔 Technical Report </a>
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<a href="https://mimo.xiaomi.com/blog/mimo-v2-flash" target="_blank">📰 Blog </a>
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<strong>Play around!</strong>
<a href="https://aistudio.xiaomimimo.com" target="_blank">🗨️ Xiaomi MiMo Studio </a>
<a href="https://platform.xiaomimimo.com/" target="_blank">🎨 Xiaomi MiMo API Platform </a>
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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.
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<img width="80%" src="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/MiMo-v2-flash-performance.jpg?raw=true">
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## 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.
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## 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.
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## 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 | 88.2Excerpt of 20,921 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cdefc330a5f82b7c, desc:agentic