Model Comparison

DeepSeek-V3.2 (Non-thinking) vs Qwen3 VL 32B Thinking

Comparing DeepSeek-V3.2 (Non-thinking) and Qwen3 VL 32B Thinking across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.2 (Non-thinking) and Qwen3 VL 32B Thinking don'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

652.0B diff

DeepSeek-V3.2 (Non-thinking) has 652.0B more parameters than Qwen3 VL 32B Thinking, making it 1975.8% larger.

DeepSeek-V3.2 (Non-thinking)

685.0Bparameters

Qwen3 VL 32B Thinking

33.0Bparameters

685.0B

DeepSeek-V3.2 (Non-thinking)

33.0B

Qwen3 VL 32B Thinking

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-V3.2 (Non-thinking)

Input131,072 tokens

Output8,192 tokens

Qwen3 VL 32B Thinking

Input- tokens

Output- tokens

Thu May 14 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 32B Thinking supports multimodal inputs, whereas DeepSeek-V3.2 (Non-thinking) does not.

Qwen3 VL 32B Thinking 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

Qwen3 VL 32B Thinking

Text

Images

Audio

Video

License

Usage and distribution terms

DeepSeek-V3.2 (Non-thinking) 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.

DeepSeek-V3.2 (Non-thinking)

MIT

Open weights

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while Qwen3 VL 32B Thinking was released on 2025-09-22.

DeepSeek-V3.2 (Non-thinking) is 2 months newer than Qwen3 VL 32B Thinking.

DeepSeek-V3.2 (Non-thinking)

Dec 1, 2025

5 months ago

2mo newer

Qwen3 VL 32B Thinking

Sep 22, 2025

7 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

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Key Takeaways

DeepSeek-V3.2 (Non-thinking)

View details

DeepSeek

Larger context window (131,072 tokens)

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature	DeepSeek-V3.2 (Non-thinking)	Qwen3 VL 32B Thinking

FAQ

Common questions about DeepSeek-V3.2 (Non-thinking) vs Qwen3 VL 32B Thinking.

Which is better, DeepSeek-V3.2 (Non-thinking) or Qwen3 VL 32B Thinking?

DeepSeek-V3.2 (Non-thinking) (DeepSeek) and Qwen3 VL 32B Thinking (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 Qwen3 VL 32B Thinking in benchmarks?

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 DeepSeek-V3.2 (Non-thinking) and Qwen3 VL 32B Thinking?

DeepSeek-V3.2 (Non-thinking) supports 131K 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 DeepSeek-V3.2 (Non-thinking) 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 DeepSeek-V3.2 (Non-thinking) and Qwen3 VL 32B Thinking?

DeepSeek-V3.2 (Non-thinking) is developed by DeepSeek and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.