Qwen 3.5 and MiMo-V2-Flash are both open-source Mixture-of-Experts models from Chinese tech companies. Qwen 3.5 is Alibaba’s flagship with 397B total parameters. MiMo-V2-Flash is Xiaomi’s speed-optimized model with 309B total parameters. Both are Apache 2.0 licensed. Both are available on HuggingFace. Both are absurdly cheap.
But they’re built for different things.
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
Benchmarks across the board. Qwen 3.5 scores higher on almost every benchmark: SWE-bench (76.4 vs 73.4), MMLU (88.6 vs ~82), AIME 2026 (91.3), and instruction following (IFBench 76.5 — highest of any model). It’s a more capable model overall.
Multimodal. Qwen 3.5 is natively multimodal — it processes text, images, and video. MiMo-V2-Flash is text-only. If you need visual understanding, document analysis, or chart reading, Qwen is the only option.
201 languages. Qwen supports 201 languages and dialects vs MiMo’s ~30. For multilingual applications, especially in Southeast Asia, South Asia, or Africa, Qwen is far more capable.
Larger context. 256K native (1M via API) vs 128K. For long documents, large codebases, or multi-turn conversations, Qwen can hold more 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 specific use case, Qwen has options.
Where MiMo-V2-Flash wins
Speed. MiMo-V2-Flash runs at 150 tokens per second. It’s specifically optimized for fast inference. For applications where latency matters — chatbots, real-time coding assistants, interactive tools — Flash is noticeably snappier.
Simplicity. One model, one size, one purpose: fast and cheap general-purpose AI. No decision paralysis about which size to use.
Output pricing. Flash charges $0.30/M output tokens vs Qwen’s similar rate, but Flash’s speed means you get results faster, which matters for throughput-sensitive applications.
Part of an integrated stack. MiMo-V2-Flash is designed to work alongside MiMo-V2-Pro (for hard reasoning), MiMo-V2-Omni (for multimodal), and MiMo-V2-TTS (for speech). If you’re building within the Xiaomi ecosystem, Flash is the fast/cheap tier of a complete AI stack.
The honest take
Qwen 3.5 is the better model. 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.
The practical approach: use Qwen 3.5 as your primary open-source model and MiMo-V2-Flash as a fast fallback for latency-sensitive tasks. Both are Apache 2.0, both are cheap, and they complement each other well.