Model Comparison

MiMo-V2-Flash vs Qwen3 VL 32B ThinkingWhich is better in 2026?

MiMo-V2-Flash significantly outperforms across most benchmarks.

Verdict: MiMo-V2-Flash vs Qwen3 VL 32B Thinking — which is better?

MiMo-V2-Flash (by Xiaomi) and Qwen3 VL 32B Thinking (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

MiMo-V2-Flash outperforms in 5 benchmarks (AIME 2025, Arena-Hard v2, GPQA, LiveCodeBench v6, MMLU-Pro), while Qwen3 VL 32B Thinking is better at 0 benchmarks. MiMo-V2-Flash significantly outperforms across most benchmarks.

Choose MiMo-V2-Flash if…

  • you want the strongest raw capability — it leads on 5 of 5 shared benchmarks
  • you want the most recent training data — it shipped Dec 2025

Choose Qwen3 VL 32B Thinking if…

  • you are already invested in the Alibaba Cloud / Qwen Team ecosystem
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Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

MiMo-V2-Flash outperforms in 5 benchmarks (AIME 2025, Arena-Hard v2, GPQA, LiveCodeBench v6, MMLU-Pro), while Qwen3 VL 32B Thinking is better at 0 benchmarks.

MiMo-V2-Flash significantly outperforms across most benchmarks.

Sun Jun 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

276.0B diff

MiMo-V2-Flash has 276.0B more parameters than Qwen3 VL 32B Thinking, making it 836.4% larger.

Xiaomi
MiMo-V2-Flash
309.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
33.0Bparameters
309.0B
MiMo-V2-Flash
33.0B
Qwen3 VL 32B Thinking

Context Window

Maximum input and output token capacity

Only MiMo-V2-Flash specifies input context (256,000 tokens). Only MiMo-V2-Flash specifies output context (16,384 tokens).

Xiaomi
MiMo-V2-Flash
Input256,000 tokens
Output16,384 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Sun Jun 14 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 32B Thinking supports multimodal inputs, whereas MiMo-V2-Flash does not.

Qwen3 VL 32B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.

MiMo-V2-Flash

Text
Images
Audio
Video

Qwen3 VL 32B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

MiMo-V2-Flash is licensed under MIT, while Qwen3 VL 32B Thinking uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

MiMo-V2-Flash

MIT

Open weights

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

MiMo-V2-Flash was released on 2025-12-16, while Qwen3 VL 32B Thinking was released on 2025-09-22.

MiMo-V2-Flash is 3 months newer than Qwen3 VL 32B Thinking.

MiMo-V2-Flash

Dec 16, 2025

6 months ago

2mo newer
Qwen3 VL 32B Thinking

Sep 22, 2025

8 months ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Xiaomi
Larger context window (256,000 tokens)
Higher AIME 2025 score (94.1% vs 83.7%)
Higher Arena-Hard v2 score (86.2% vs 60.5%)
Higher GPQA score (83.7% vs 73.1%)
Higher LiveCodeBench v6 score (80.6% vs 65.6%)
Higher MMLU-Pro score (84.9% vs 82.1%)
Alibaba Cloud / Qwen Team

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
Xiaomi
MiMo-V2-Flash
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking

FAQ

Common questions about MiMo-V2-Flash vs Qwen3 VL 32B Thinking.

Which is better, MiMo-V2-Flash or Qwen3 VL 32B Thinking?

MiMo-V2-Flash significantly outperforms across most benchmarks. MiMo-V2-Flash is made by Xiaomi and Qwen3 VL 32B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does MiMo-V2-Flash compare to Qwen3 VL 32B Thinking in benchmarks?

MiMo-V2-Flash scores AIME 2025: 94.1%, Arena-Hard v2: 86.2%, MMLU-Pro: 84.9%, HMMT 2025: 84.4%, GPQA: 83.7%. Qwen3 VL 32B Thinking scores DocVQAtest: 96.1%, ScreenSpot: 95.7%, MMLU-Redux: 91.9%, MMBench-V1.1: 90.8%, CharXiv-D: 90.2%.

What are the context window sizes for MiMo-V2-Flash and Qwen3 VL 32B Thinking?

MiMo-V2-Flash supports 256K tokens and Qwen3 VL 32B Thinking supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between MiMo-V2-Flash and Qwen3 VL 32B Thinking?

Key differences include multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes MiMo-V2-Flash and Qwen3 VL 32B Thinking?

MiMo-V2-Flash is developed by Xiaomi and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.

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