What is MiMo-V2-Flash? Xiaomi's Open-Source Speed Demon Explained
📢 Update: MiMo V2.5 Pro is now available — significantly improved over V2. See the V2.5 complete guide, how to use the API, and V2.5 vs V2 Pro comparison.
MiMo-V2-Flash is Xiaomi’s open-source AI model, released in December 2025. While its bigger sibling MiMo-V2-Pro grabbed headlines by being mistaken for DeepSeek V4, Flash quietly became one of the most popular open-source models for developers who want to self-host or need ultra-cheap inference.
Update (April 23, 2026): Xiaomi released the MiMo V2.5 series, including MiMo V2.5 Pro, which scores 57.2% on SWE-bench Pro and uses 40-60% fewer tokens than Opus 4.6. See our V2.5 Pro complete guide for details.
The specs
| MiMo-V2-Flash | |
|---|---|
| Architecture | Mixture-of-Experts (MoE) |
| Total parameters | 309B |
| Active parameters | 15B per token |
| Context window | 56K tokens |
| Speed | 150 tokens/sec |
| Pricing (API) | $0.10/$0.30 per million tokens |
| Open source | Yes (HuggingFace) |
| SWE-Bench Verified | 73.4% (#1 open-source) |
The key insight: 309B total parameters but only 15B active per request. That’s the MoE trick — you get the knowledge of a massive model with the inference cost of a small one.
What makes it special
It’s the fastest model in its class. 150 tokens per second is significantly faster than most models at this capability level. The hybrid sliding-window attention architecture (128-token window, 5:1 ratio) is what enables this — it processes nearby tokens cheaply and only uses full attention for long-range dependencies.
It’s genuinely good at coding. 73.4% on SWE-Bench Verified makes it the #1 open-source model for real-world coding tasks. That’s comparable to Claude Sonnet 4.5 — a closed-source model that costs roughly 30x more.
Multi-Token Prediction (MTP). Instead of predicting one token at a time, Flash predicts multiple tokens simultaneously. This is a key reason for the speed advantage.
It’s actually open source. Weights are on HuggingFace. You can download it, run it locally, fine-tune it. No API dependency required.
How it compares
| Model | SWE-Bench | Pricing (in/out) | Open source |
|---|---|---|---|
| MiMo-V2-Flash | 73.4% | $0.10/$0.30 | ✅ Yes |
| MiMo-V2-Pro | ~80%+ | $1.00/$3.00 | ❌ No |
| DeepSeek V3.2 | 65.4% | $0.28/$1.10 | ✅ Yes |
| Claude Sonnet 4.5 | 72.8% | $3.00/$15.00 | ❌ No |
| Claude Opus 4.6 | 84.2% | $5.00/$25.00 | ❌ No |
Flash sits in a sweet spot: better than DeepSeek V3.2 on coding, comparable to Claude Sonnet, and dramatically cheaper than both closed-source options.
When to use MiMo-V2-Flash
Use Flash when:
- You need fast, cheap inference at scale
- You want to self-host and control your data
- Coding tasks where “good enough” beats “perfect”
- High-volume processing where cost matters more than peak quality
- You’re building prototypes and iterating quickly
Use MiMo-V2-Pro instead when:
- You need the best possible agent performance
- Complex multi-step workflows requiring deep reasoning
- Tasks that benefit from the 1M token context window
- You don’t need open-source weights
Use Claude/GPT instead when:
- Absolute accuracy is critical
- You need the most reliable instruction following
- Enterprise compliance requirements
How to access it
Via API (cheapest): Available on OpenRouter at $0.10/$0.30 per million tokens. Uses the standard OpenAI-compatible format.
Self-hosted: Download weights from HuggingFace. Requires significant GPU resources due to the 309B total parameter count, but the 15B active parameters mean inference is manageable on modern hardware.
Free tiers: Several platforms offer free access including Kilo Code and Puter.js.
The bottom line
MiMo-V2-Flash is the model that makes you question why you’re paying for closed-source APIs. It’s not the best model available — MiMo-V2-Pro and Claude Opus are both better. But it’s open source, blazing fast, and costs almost nothing. For the majority of development tasks, that’s more than enough.
Related: MiMo-V2-Pro vs MiMo-V2-Flash — Which Xiaomi Model Should You Use?
Related: The Complete MiMo-V2 Family Guide — Pro, Flash, Omni, and TTS
FAQ
Can I self-host MiMo-V2-Flash?
Yes. The full 309B parameter weights are available on HuggingFace. However, despite only 15B parameters being active per request, you still need to load the full model into memory. This requires significant GPU resources — typically a multi-GPU setup with 80GB+ total VRAM. For most developers, the API at $0.10/$0.30 per million tokens is more practical.
How does MiMo-V2-Flash achieve 150 tokens per second?
Three architectural innovations enable the speed: Multi-Token Prediction (predicting multiple tokens simultaneously), hybrid sliding-window attention (128-token window with a 5:1 ratio for cheap local processing), and the MoE architecture that only activates 15B of 309B parameters per token. Together, these reduce compute per token dramatically.
Should I use MiMo-V2-Flash or DeepSeek V3 for coding?
MiMo-V2-Flash scores 73.4% on SWE-bench vs DeepSeek V3.2’s 65.4%, making it significantly better for real-world coding tasks. It’s also faster (150 tok/s vs ~60 tok/s) and cheaper ($0.10/$0.30 vs $0.28/$1.10). Flash is the better choice for coding unless you specifically need DeepSeek’s larger context window or reasoning capabilities.
Related: MiMo-V2-Flash vs DeepSeek V3 — Open-Source AI Showdown