Model Comparison

DeepSeek-V3.1 vs Qwen3 VL 32B ThinkingWhich is better in 2026?

DeepSeek-V3.1 has a slight edge in benchmark performance.

Verdict: DeepSeek-V3.1 vs Qwen3 VL 32B Thinking — which is better?

DeepSeek-V3.1 (by DeepSeek) 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.

DeepSeek-V3.1 outperforms in 3 benchmarks (GPQA, MMLU-Pro, SimpleQA), while Qwen3 VL 32B Thinking is better at 2 benchmarks (AIME 2025, MMLU-Redux). DeepSeek-V3.1 has a slight edge in benchmark performance.

Choose DeepSeek-V3.1 if…

  • you want the strongest raw capability — it leads on 3 of 5 shared benchmarks

Choose Qwen3 VL 32B Thinking if…

  • you want the most recent training data — it shipped Sep 2025
Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

DeepSeek-V3.1 outperforms in 3 benchmarks (GPQA, MMLU-Pro, SimpleQA), while Qwen3 VL 32B Thinking is better at 2 benchmarks (AIME 2025, MMLU-Redux).

DeepSeek-V3.1 has a slight edge in benchmark performance.

Sun Jun 07 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

638.0B diff

DeepSeek-V3.1 has 638.0B more parameters than Qwen3 VL 32B Thinking, making it 1933.3% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
33.0Bparameters
671.0B
DeepSeek-V3.1
33.0B
Qwen3 VL 32B Thinking

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Sun Jun 07 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 32B Thinking supports multimodal inputs, whereas DeepSeek-V3.1 does not.

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

DeepSeek-V3.1

Text
Images
Audio
Video

Qwen3 VL 32B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.1 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-V3.1

MIT

Open weights

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Qwen3 VL 32B Thinking was released on 2025-09-22.

Qwen3 VL 32B Thinking is 9 months newer than DeepSeek-V3.1.

DeepSeek-V3.1

Jan 10, 2025

1.4 years ago

Qwen3 VL 32B Thinking

Sep 22, 2025

8 months ago

8mo newer

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 (163,840 tokens)
Higher GPQA score (74.9% vs 73.1%)
Higher MMLU-Pro score (83.7% vs 82.1%)
Higher SimpleQA score (93.4% vs 55.4%)
Alibaba Cloud / Qwen Team

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs
Higher AIME 2025 score (83.7% vs 49.8%)
Higher MMLU-Redux score (91.9% vs 91.8%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking

FAQ

Common questions about DeepSeek-V3.1 vs Qwen3 VL 32B Thinking.

Which is better, DeepSeek-V3.1 or Qwen3 VL 32B Thinking?

DeepSeek-V3.1 has a slight edge in benchmark performance. DeepSeek-V3.1 is made by DeepSeek 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 DeepSeek-V3.1 compare to Qwen3 VL 32B Thinking in benchmarks?

DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. 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 DeepSeek-V3.1 and Qwen3 VL 32B Thinking?

DeepSeek-V3.1 supports 164K 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-V3.1 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-V3.1 and Qwen3 VL 32B Thinking?

DeepSeek-V3.1 is developed by DeepSeek and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.

Related comparisons