📢 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.
Qwen 3.5 and MiMo-V2-Flash are both open-source Mixture-of-Experts models from Chinese tech companies. Both are licensed under Apache 2.0. Both are available on HuggingFace. Both are absurdly cheap.
Qwen 3.5 is Alibaba’s flagship with 397B total parameters, built for maximum capability across every task type. MiMo-V2-Flash is Xiaomi’s speed-optimized model with 309B parameters, built to be as fast and cheap as possible while staying competitive on quality.
They complement each other well, but if you can only pick one, here’s how to decide. For a broader view, check our AI model comparison.
Quick Comparison
| Qwen 3.5-397B | MiMo-V2-Flash | |
|---|---|---|
| Company | Alibaba | Xiaomi |
| Total parameters | 397B | 309B |
| Active parameters | 17B | 15B |
| Context window | 256K (1M via API) | 128K |
| SWE-bench Verified | 76.4% | 73.4% |
| MMLU | 88.6% | ~82% |
| Multimodal | Yes (native vision) | No (text only) |
| Languages | 201 | ~30 |
| API input price | ~$0.11/M | $0.10/M |
| API output price | ~$0.11/M | $0.30/M |
| Inference speed | Fast | Very fast (150 tok/s) |
| License | Apache 2.0 | Apache 2.0 |
| Release | Feb 16, 2026 | Mar 18, 2026 |
Where Qwen 3.5 Wins
Benchmark performance. Qwen 3.5 scores higher on virtually every major benchmark: SWE-bench (76.4 vs 73.4), MMLU (88.6 vs ~82), AIME 2026 (91.3), and instruction following (IFBench 76.5, the highest of any model). The gap is most pronounced on reasoning-heavy tasks.
Multimodal input. Qwen 3.5 is natively multimodal — text, images, and video in a single model. MiMo-V2-Flash is text-only. If you need document understanding, chart analysis, or any visual task, Qwen is the only option.
For more on how Qwen compares to its successor, see Qwen 3.6 vs 3.5.
Language coverage. Qwen supports 201 languages and dialects compared to MiMo’s ~30. For multilingual applications targeting underserved communities, Qwen is dramatically more capable.
Larger context window. 256K native (1M via API) versus 128K. For long documents, large codebases, or extended conversations, Qwen holds twice as much in memory.
Model family depth. Qwen 3.5 comes in 8 sizes from 0.8B to 397B. MiMo-V2-Flash is a single model. If you need a tiny model for edge deployment or a medium model for a laptop, Qwen has a variant for every scenario.
For running smaller variants on consumer hardware, see our guide on best AI models under 16GB VRAM.
Where MiMo-V2-Flash Wins
Raw speed. MiMo-V2-Flash runs at 150 tokens per second, specifically optimized for fast inference. For chatbots, real-time coding assistants, and interactive tools, Flash is noticeably snappier. The speed advantage comes from its architecture being purpose-built for throughput.
For a deeper look at what makes Flash tick, see our What is MiMo V2 Flash explainer.
Simplicity. One model, one size, one purpose: fast and cheap general-purpose AI. No decision paralysis about which variant to use. You deploy Flash and it works.
Throughput economics. While per-token pricing is similar, Flash’s speed means you process more tokens per second per GPU. For applications serving many concurrent users, Flash delivers better infrastructure economics even when token prices are comparable.
Integrated ecosystem. MiMo-V2-Flash is the fast/cheap tier of Xiaomi’s complete AI stack, working alongside Pro (hard reasoning), Omni (multimodal), and TTS (speech). If you’re building within the Xiaomi ecosystem, Flash fills a well-defined role.
Running Both Locally
Both are open-source under Apache 2.0 and available on HuggingFace.
For local deployment:
- Qwen 3.5 smaller variants (0.8B, 1.5B, 4B, 8B) run on consumer GPUs with 8-16GB VRAM. The 32B and 72B need multi-GPU setups. The full 397B requires a cluster.
- MiMo-V2-Flash at 309B total is demanding for the full model, but with only 15B active parameters, quantized versions run on more modest hardware. Its speed optimization means it performs well with inference engines like vLLM.
Both benefit from GGUF quantization. The active parameter counts (17B for Qwen, 15B for Flash) are close enough that local performance is comparable on the same hardware, though Flash’s optimization gives it an edge in tokens-per-second.
Use Case Routing
The smartest approach for many teams is to use both models together:
- Complex reasoning, multimodal, multilingual → Qwen 3.5
- Fast responses, high throughput, latency-sensitive → MiMo-V2-Flash
- Edge deployment on limited hardware → Qwen 3.5 smaller variants (0.8B–8B)
This routing pattern maximizes quality where it matters while keeping latency low for interactive use cases.
The Honest Take
Qwen 3.5 is the better model by most measures. It scores higher on benchmarks, supports more languages, handles multimodal input, and has a larger context window. If you’re choosing one open-source model for general use, Qwen 3.5 is the stronger pick.
MiMo-V2-Flash is the faster, simpler option. If you need raw speed at rock-bottom pricing and don’t need vision or 200+ languages, Flash does the job with less overhead and lower latency.
Both are Apache 2.0, both are cheap, and they complement each other naturally. There’s no wrong choice — only different priorities.
FAQ
Is MiMo V2 Flash better than Qwen 3.5?
Not overall. Qwen 3.5 scores higher on benchmarks, supports multimodal input, covers 201 languages, and has a larger context window. However, MiMo-V2-Flash is faster (150 tokens/second) and optimized for low-latency applications. Flash is better for speed-critical tasks; Qwen is better for everything else.
Which is better for local use?
Both are open-source and available for local deployment. MiMo-V2-Flash has a slight edge due to its lower active parameter count (15B vs 17B) and speed optimization. However, Qwen 3.5 offers smaller variants (down to 0.8B) that run on much more modest hardware. For constrained environments, Qwen’s smaller models are more practical.
Are both open source?
Yes. Both Qwen 3.5 and MiMo-V2-Flash are released under the Apache 2.0 license. You can download, modify, fine-tune, and deploy both commercially without restrictions. Both are available on HuggingFace with full model weights.
Which is faster?
MiMo-V2-Flash is faster. It’s specifically optimized for inference speed at approximately 150 tokens per second. Qwen 3.5 is fast but not purpose-built for speed in the same way. For latency-sensitive applications like chatbots or real-time coding assistants, Flash has a clear advantage.