Model Comparison

DeepSeek-V3.2 (Thinking) vs Qwen3 VL 32B InstructWhich is better in 2026?

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Verdict: DeepSeek-V3.2 (Thinking) vs Qwen3 VL 32B Instruct — which is better?

DeepSeek-V3.2 (Thinking) (by DeepSeek) and Qwen3 VL 32B Instruct (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.

DeepSeek-V3.2 (Thinking) outperforms in 3 benchmarks (AIME 2025, GPQA, MMLU-Pro), while Qwen3 VL 32B Instruct is better at 0 benchmarks. DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Choose DeepSeek-V3.2 (Thinking) if…

  • you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
  • you want the most recent training data — it shipped Dec 2025

Choose Qwen3 VL 32B Instruct if…

  • you are already invested in the Alibaba Cloud / Qwen Team ecosystem
Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek-V3.2 (Thinking) outperforms in 3 benchmarks (AIME 2025, GPQA, MMLU-Pro), while Qwen3 VL 32B Instruct is better at 0 benchmarks.

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.

Sun Jun 21 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

652.0B diff

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

DeepSeek
DeepSeek-V3.2 (Thinking)
685.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Instruct
33.0Bparameters
685.0B
DeepSeek-V3.2 (Thinking)
33.0B
Qwen3 VL 32B Instruct

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2 (Thinking) specifies input context (131,072 tokens). Only DeepSeek-V3.2 (Thinking) specifies output context (65,536 tokens).

DeepSeek
DeepSeek-V3.2 (Thinking)
Input131,072 tokens
Output65,536 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Instruct
Input- tokens
Output- tokens
Sun Jun 21 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

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

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

DeepSeek-V3.2 (Thinking)

Text
Images
Audio
Video

Qwen3 VL 32B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2 (Thinking) is licensed under MIT, while Qwen3 VL 32B Instruct uses Apache 2.0.

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

DeepSeek-V3.2 (Thinking)

MIT

Open weights

Qwen3 VL 32B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

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

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

DeepSeek-V3.2 (Thinking)

Dec 1, 2025

6 months ago

2mo newer
Qwen3 VL 32B Instruct

Sep 22, 2025

9 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

DeepSeek
Larger context window (131,072 tokens)
Higher AIME 2025 score (93.1% vs 66.2%)
Higher GPQA score (82.4% vs 68.9%)
Higher MMLU-Pro score (85.0% vs 78.6%)
Alibaba Cloud / Qwen Team

Qwen3 VL 32B Instruct

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2 (Thinking)
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Instruct

FAQ

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

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

DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. DeepSeek-V3.2 (Thinking) is made by DeepSeek and Qwen3 VL 32B Instruct 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 DeepSeek-V3.2 (Thinking) compare to Qwen3 VL 32B Instruct in benchmarks?

DeepSeek-V3.2 (Thinking) scores AIME 2025: 93.1%, HMMT 2025: 90.2%, MMLU-Pro: 85.0%, LiveCodeBench: 83.3%, GPQA: 82.4%. Qwen3 VL 32B Instruct scores DocVQAtest: 96.9%, ScreenSpot: 95.8%, CharXiv-D: 90.5%, MMLU-Redux: 89.8%, AI2D: 89.5%.

What are the context window sizes for DeepSeek-V3.2 (Thinking) and Qwen3 VL 32B Instruct?

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

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 (Thinking) and Qwen3 VL 32B Instruct?

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

Related comparisons