Model Comparison

DeepSeek-V3.2 (Non-thinking) vs QvQ-72B-PreviewWhich is better in 2026?

Comparing DeepSeek-V3.2 (Non-thinking) and QvQ-72B-Preview across benchmarks, pricing, and capabilities.

Verdict: DeepSeek-V3.2 (Non-thinking) vs QvQ-72B-Preview — which is better?

DeepSeek-V3.2 (Non-thinking) (by DeepSeek) and QvQ-72B-Preview (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.

Choose DeepSeek-V3.2 (Non-thinking) if…

  • you want the most recent training data — it shipped Dec 2025

Choose QvQ-72B-Preview if…

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

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.2 (Non-thinking) and QvQ-72B-Previewdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

611.6B diff

DeepSeek-V3.2 (Non-thinking) has 611.6B more parameters than QvQ-72B-Preview, making it 833.2% larger.

DeepSeek
DeepSeek-V3.2 (Non-thinking)
685.0Bparameters
Alibaba Cloud / Qwen Team
QvQ-72B-Preview
73.4Bparameters
685.0B
DeepSeek-V3.2 (Non-thinking)
73.4B
QvQ-72B-Preview

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2 (Non-thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Non-thinking) specifies output context (8,192 tokens).

DeepSeek
DeepSeek-V3.2 (Non-thinking)
Input131,072 tokens
Output8,192 tokens
Alibaba Cloud / Qwen Team
QvQ-72B-Preview
Input- tokens
Output- tokens
Mon Jul 27 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

QvQ-72B-Preview supports multimodal inputs, whereas DeepSeek-V3.2 (Non-thinking) does not.

QvQ-72B-Preview can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.2 (Non-thinking)

Text
Images
Audio
Video

QvQ-72B-Preview

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2 (Non-thinking) is licensed under MIT, while QvQ-72B-Preview uses Qwen.

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

DeepSeek-V3.2 (Non-thinking)

MIT

Open weights

QvQ-72B-Preview

Qwen

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while QvQ-72B-Preview was released on 2024-12-25.

DeepSeek-V3.2 (Non-thinking) is 11 months newer than QvQ-72B-Preview.

DeepSeek-V3.2 (Non-thinking)

Dec 1, 2025

7 months ago

11mo newer
QvQ-72B-Preview

Dec 25, 2024

1.6 years 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

DeepSeek
Larger context window (131,072 tokens)
Alibaba Cloud / Qwen Team

QvQ-72B-Preview

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against DeepSeek-V3.2 (Non-thinking) and QvQ-72B-Preview side-by-side, then vote on the output you prefer.

DeepSeek-V3.2 (Non-thinking)
✓ Preferred
QvQ-72B-Preview
Open in Playground
AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2 (Non-thinking)
Alibaba Cloud / Qwen Team
QvQ-72B-Preview

FAQ

Common questions about DeepSeek-V3.2 (Non-thinking) vs QvQ-72B-Preview.

Which is better, DeepSeek-V3.2 (Non-thinking) or QvQ-72B-Preview?

DeepSeek-V3.2 (Non-thinking) (DeepSeek) and QvQ-72B-Preview (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-V3.2 (Non-thinking) compare to QvQ-72B-Preview in benchmarks?

QvQ-72B-Preview scores MathVista: 71.4%, MMMU: 70.3%, MathVision: 35.9%, OlympiadBench: 20.4%.

What are the context window sizes for DeepSeek-V3.2 (Non-thinking) and QvQ-72B-Preview?

DeepSeek-V3.2 (Non-thinking) supports 131K tokens and QvQ-72B-Preview 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 DeepSeek-V3.2 (Non-thinking) and QvQ-72B-Preview?

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

Who makes DeepSeek-V3.2 (Non-thinking) and QvQ-72B-Preview?

DeepSeek-V3.2 (Non-thinking) is developed by DeepSeek and QvQ-72B-Preview is developed by Alibaba Cloud / Qwen Team.

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