Qwen 3.5 and MiMo-V2-Pro are both frontier-class AI models from Chinese tech companies. Qwen 3.5 is Alibaba’s open-source flagship with 397B parameters. MiMo-V2-Pro is Xiaomi’s closed-source agent model with over 1 trillion parameters. They represent two very different strategies for building frontier AI.
Quick comparison
| Qwen 3.5-397B | MiMo-V2-Pro | |
|---|---|---|
| Company | Alibaba | Xiaomi |
| Total parameters | 397B | 1T+ |
| Active parameters | 17B | 42B |
| Context window | 256K (1M via API) | 1M |
| SWE-bench Verified | 76.4% | 78% |
| AIME 2026 | 91.3 | ~85 |
| Multimodal | Yes (native vision) | No (text only, Omni is separate) |
| Languages | 201 | ~30 |
| API input price | ~$0.11/M | $1.00/M |
| API output price | ~$0.11/M | $3.00/M |
| License | Apache 2.0 | Closed-source API |
| Open-source | Yes | No |
| Agent ranking | Competitive | #3 globally |
Where Qwen 3.5 wins
Open-source. Qwen 3.5 is fully Apache 2.0. You can download it, self-host it, fine-tune it, embed it in products. MiMo-V2-Pro is closed-source and API-only. For developers who need control over their AI stack, this is a dealbreaker.
Price. Qwen costs ~$0.11/M tokens. MiMo-V2-Pro costs $1/$3. That’s roughly 10-27x more expensive. For high-volume workloads, the cost difference is massive.
Multimodal. Qwen 3.5 is natively multimodal — text, images, and video in one model. MiMo-V2-Pro is text-only. Xiaomi has MiMo-V2-Omni for multimodal, but that’s a separate model.
201 languages. Qwen supports 201 languages and dialects. MiMo focuses on ~30. For multilingual applications, Qwen is far more capable.
Math reasoning. Qwen scores 91.3 on AIME 2026, which is higher than MiMo-V2-Pro’s estimated ~85. On pure mathematical reasoning, Qwen has an edge.
Model family. Qwen comes in 8 sizes from 0.8B to 397B. MiMo-V2-Pro 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-Pro wins
Agent tasks. MiMo-V2-Pro ranks #3 globally on agent benchmarks, right behind Claude Opus 4.6. It’s specifically designed for autonomous AI agent workflows — multi-step planning, tool use, and complex task execution. This is its primary strength.
SWE-bench. MiMo-V2-Pro scores 78% on SWE-bench Verified vs Qwen’s 76.4%. For real-world coding tasks that require understanding large codebases and making multi-file changes, Pro has a slight edge.
1M context window. Both offer 1M tokens via API, but MiMo-V2-Pro’s architecture is specifically optimized for long-context agent tasks. The 1T parameter count with 42B active gives it more capacity for complex reasoning within that context.
The stealth launch. MiMo-V2-Pro spent a week on OpenRouter as “Hunter Alpha” and was mistaken for DeepSeek V4. That blind test is the closest thing to independent validation — it performed at frontier level before anyone knew who made it.
Integrated stack. MiMo-V2-Pro is designed to work with Flash (fast/cheap), Omni (multimodal), and TTS (speech). If you’re building a complete AI system, the MiMo family covers all the bases.
The honest take
These models serve different purposes:
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Qwen 3.5 is the better general-purpose model. It’s open-source, cheaper, multimodal, supports more languages, and scores higher on most benchmarks. If you’re picking one model for everything, Qwen wins.
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MiMo-V2-Pro is the better agent model. If you’re building autonomous AI systems that need to plan, execute multi-step tasks, and use tools, Pro’s #3 global ranking on agent benchmarks matters more than Qwen’s broader benchmark scores.
The practical approach: use Qwen 3.5 for general tasks and MiMo-V2-Pro for agent-specific workloads. Or use Qwen 3.5 for everything and save 10-27x on costs, accepting a small quality tradeoff on agent tasks.