📢 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-Pro represent two very different strategies for building frontier AI.

Qwen 3.5 is Alibaba’s open-source flagship with 397B parameters, designed as a general-purpose powerhouse. MiMo-V2-Pro is Xiaomi’s closed-source agent model with over 1 trillion parameters, purpose-built for autonomous task execution.

Choosing between them depends on whether you need a versatile generalist or a specialized agent. For a broader view of how these stack up, see our AI model comparison.

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)
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
Agent ranking	Competitive	#3 globally

Where Qwen 3.5 Wins

Open-source availability. Qwen 3.5 is fully Apache 2.0 licensed. You can download it, self-host it, fine-tune it, and embed it in commercial products. MiMo-V2-Pro is closed-source and API-only.

For developers who need control over their AI stack, this is often a dealbreaker.

Pricing. Qwen costs ~$0.11/M tokens for both input and output. MiMo-V2-Pro costs $1/$3 per million tokens — roughly 10-27x more expensive.

For high-volume workloads, the cost difference is massive. If budget is a primary concern, see our best cheap AI model 2026 guide.

Multimodal capabilities. Qwen 3.5 handles text, images, and video natively. MiMo-V2-Pro is text-only. Xiaomi offers MiMo-V2-Omni for multimodal tasks, but that’s a separate model.

Language support. Qwen supports 201 languages and dialects versus MiMo’s ~30. For multilingual applications, Qwen is dramatically more capable.

Math reasoning. Qwen scores 91.3 on AIME 2026 compared to MiMo-V2-Pro’s estimated ~85. On pure mathematical reasoning, Qwen has a clear edge.

Model family depth. Qwen comes in 8 sizes from 0.8B to 397B, covering every deployment scenario. MiMo-V2-Pro is a single model.

For how Qwen’s versions compare, see Qwen 3.6 vs 3.5.

Where MiMo-V2-Pro Wins

Agent tasks. This is MiMo-V2-Pro’s primary strength. It ranks #3 globally on agent benchmarks, right behind Claude Opus 4.6. It’s specifically designed for autonomous workflows — multi-step planning, tool use, error recovery, and complex task execution.

If you’re building systems that need to act independently, Pro is purpose-built for that job.

For more on the full MiMo family, see our MiMo V2 family guide.

SWE-bench coding. MiMo-V2-Pro scores 78% on SWE-bench Verified versus Qwen’s 76.4%. For real-world coding tasks requiring large codebase understanding and multi-file changes, Pro has a measurable edge.

1M native context. 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 window.

Blind test validation. MiMo-V2-Pro spent a week on OpenRouter under the alias “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 ecosystem. MiMo-V2-Pro works alongside Flash (fast/cheap), Omni (multimodal), and TTS (speech). If you’re building a complete AI system, the MiMo family covers all bases.

Deployment Considerations

Qwen 3.5 gives you full deployment flexibility. Self-host on your own infrastructure, run smaller variants on consumer hardware, or use the API. You control the data pipeline end to end.

MiMo-V2-Pro is API-only. Your data goes through Xiaomi’s servers. For teams with strict data residency or privacy requirements, this limits your options.

If local deployment matters, Qwen is your only choice between these two.

The Honest Take

These models serve fundamentally different purposes.

Qwen 3.5 is the better general-purpose model. It’s open-source, dramatically cheaper, multimodal, supports more languages, and scores higher on most benchmarks. If you’re picking one model for everything, Qwen wins.

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 reliably, Pro’s #3 global agent ranking matters more than Qwen’s broader benchmark advantages.

The practical approach: use Qwen 3.5 for general tasks and consider MiMo-V2-Pro specifically for agent-heavy workloads where its specialized capabilities justify the 10-27x price premium.

FAQ

Is Qwen 3.5 better than MiMo V2 Pro?

For general-purpose tasks, yes. Qwen 3.5 is open-source, 10-27x cheaper, supports multimodal input, covers 201 languages, and scores higher on math reasoning. However, MiMo-V2-Pro is better for agentic tasks (ranked #3 globally) and scores slightly higher on SWE-bench coding (78% vs 76.4%).

Which is cheaper?

Qwen 3.5 is dramatically cheaper at ~$0.11/M tokens for both input and output, versus MiMo-V2-Pro’s $1/$3 per million tokens. That makes Qwen roughly 10x cheaper on input and 27x cheaper on output. Qwen is also open-source, so you can self-host to eliminate per-token charges entirely.

Can I run both locally?

You can run Qwen 3.5 locally since it’s open-source under Apache 2.0. Smaller variants (0.8B to 72B) run on consumer hardware, while the full 397B needs GPU clusters. MiMo-V2-Pro is closed-source and API-only — local deployment is not an option.

Which is better for agentic tasks?

MiMo-V2-Pro is specifically designed for agentic workflows and ranks #3 globally on agent benchmarks. It excels at multi-step planning, tool use, and autonomous task execution. Qwen 3.5 is competitive but wasn’t purpose-built for agents. If agentic capability is your primary requirement, MiMo-V2-Pro is the stronger choice.