MiMo-V2-Flash: Xiaomi's 309B MoE Open-Weight Model Guide
Xiaomi has entered the frontier AI race with MiMo-V2-Flash, a 309B parameter MoE model that achieves state-of-the-art open-source performance on software engineering benchmarks while running at 150 tokens per second.
Total Parameters
Active Parameters
SWE-Bench Verified
Inference Speed
Key Takeaways
MiMo-V2-Flash represents Xiaomi's ambitious entry into frontier AI development. The 309B parameter Mixture-of-Experts model achieves 73.4% on SWE-Bench Verified—state-of-the-art for open-source models—while activating only 15B parameters per token. This architecture enables inference speeds of 150 tokens per second, making it practical for real-time coding assistance and agentic workflows where latency directly impacts productivity.
The model's technical innovations include Hybrid Sliding Window Attention (SWA) that outperformed linear attention variants, 3-layer multi-token prediction enabling ~2.5x speedup through speculative decoding, and a 256K context window for processing entire codebases. Perhaps most significantly for developers, MiMo is available free on OpenRouter with day-0 SGLang support for optimized serving.
What is MiMo-V2-Flash
MiMo-V2-Flash is Xiaomi's flagship large language model, released December 2025. The "MiMo" name reflects Xiaomi's internal AI research division, while "V2-Flash" indicates this is the speed-optimized second-generation variant. The model targets agentic coding workflows where inference speed and cost directly impact productivity.
The 309B MoE architecture means the model contains 309 billion total parameters distributed across expert networks, but only 15 billion activate for any given token. This sparse activation pattern enables frontier-class capability at a fraction of the inference cost of dense models like GPT-4 or Claude. The efficiency gains compound over long conversations and complex agentic loops.
- Cost Efficiency: Only 15B of 309B parameters compute per token, reducing inference costs by ~20x vs equivalent dense model
- Speed: Smaller active parameter count enables 150 tok/s inference with speculative decoding
- Capability: Total 309B parameters provide frontier-level knowledge and reasoning
- Scalability: Router learns which experts to activate per task, enabling specialization
Architecture Innovations
MiMo's technical report details several architectural innovations that emerged from extensive ablation studies. These aren't incremental improvements but fundamental design choices that differentiate MiMo from other MoE models.
Combines sparse local windows with global attention layers for efficient long-context processing.
- Window size 128 beats 512 post-training
- Outperformed linear attention variants
- Attention sinks critical for stability
Predicts multiple future tokens simultaneously for speculative decoding speedup.
- 3-layer MTP architecture
- >3 accept length average
- ~2.5x speedup on coding tasks
Benchmark Performance
MiMo-V2-Flash achieves state-of-the-art open-source performance on software engineering benchmarks, competing with models many times its effective size.
| Benchmark | MiMo-V2-Flash | DeepSeek-V3.2 | GPT-4 |
|---|---|---|---|
| SWE-Bench Verified | 73.4% | ~70% | ~65% |
| SWE-Bench Multilingual | 71.7% | ~68% | ~62% |
| LiveCodeBench v5 | Top tier | Comparable | Strong |
| Inference Speed | 150 tok/s | ~30 tok/s | ~40 tok/s |
MiMo vs DeepSeek: Detailed Comparison
Both MiMo-V2-Flash and DeepSeek-V3.2 represent the frontier of open-weight coding models, but they make different architectural tradeoffs.
| Aspect | MiMo-V2-Flash | DeepSeek-V3.2 |
|---|---|---|
| Architecture | 309B MoE (15B active) | 671B MoE |
| Speed | 150 tok/s (faster) | ~30 tok/s |
| Context | 256K tokens | 128K tokens |
| SWE-Bench | 73.4% (higher) | ~70% |
| Best For | Speed-critical coding | Complex reasoning |
- Speed is critical for your workflow
- You need 256K context for large codebases
- Running many agentic iterations
- Cost optimization is a priority
- Maximum reasoning depth needed
- You have existing DeepSeek integrations
- Broader general knowledge required
- Speed is less critical than quality
Getting Started
MiMo-V2-Flash is accessible through multiple channels, from zero-setup cloud APIs to self-hosted deployments.
- Visit openrouter.ai
- Select MiMo-V2-Flash model
- Free for limited time
- OpenAI-compatible API
- Day-0 SGLang support
- Speculative decoding enabled
- Full speed optimization
- Requires GPU infrastructure
Best Use Cases
- Fast iteration loops with tool use
- Multi-step code generation
- SWE-Bench validated performance
- 256K context for entire projects
- Cross-file understanding
- Documentation generation
- 15B active params = lower inference cost
- Free on OpenRouter (limited time)
- Self-hosting option available
- 150 tok/s enables responsive UX
- IDE integration viable
- Interactive coding sessions
When NOT to Use MiMo-V2-Flash
- Non-coding tasks
Optimized for code; use general models for other tasks
- Mission-critical production (yet)
New model; evaluate thoroughly before deployment
- Regulatory-constrained environments
Chinese origin may have compliance implications
- Speed-critical coding
150 tok/s makes iteration loops fast
- Open-source requirements
Open-weight with commercial license
- Cost-conscious deployments
MoE architecture reduces inference costs
Common Mistakes to Avoid
Assuming Dense Model Behavior
Mistake: Expecting MiMo to behave like a 309B dense model.
Fix: Understand only 15B params activate; effective capability is between 15B and 309B.
Not Using Speculative Decoding
Mistake: Running MiMo without MTP, missing the ~2.5x speed advantage.
Fix: Use SGLang or compatible frameworks that enable multi-token prediction.
Ignoring Context Window Benefits
Mistake: Truncating context when 256K is available.
Fix: Leverage full context for codebase understanding and complex tasks.
Using for Non-Coding Tasks
Mistake: Expecting strong performance on general knowledge tasks.
Fix: MiMo is optimized for coding; use general models for other tasks.
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