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DeepSeek-V3 vs Qwen3 VL 32B Thinking

Qwen3 VL 32B Thinking leads the LLM Stats Score 23.5 to 15.6.

DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Qwen3 VL 32B Thinking leads the overall LLM Stats Score 23.5 to 15.6, ranking #189 overall.

In the 6 individual benchmarks reported for both models, Qwen3 VL 32B Thinking wins 6; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek-V3

  • you want predictable pricing at $0.27/M input and $0.89/M output

Choose Qwen3 VL 32B Thinking

  • overall performance matters — it scores 23.5 and ranks #189 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 6 of 6 exact shared results
  • you want the most recent training data — it shipped Sep 2025

At a glance

The differences that matter most.

DeepSeek-V3
DeepSeek
Qwen3 VL 32B Thinking
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
15.6
#244
23.5
#189
Reasoning
14.7
#243
24.6
#170
Cost, coverage & limits
Benchmark wins
0 of 6
6 of 6
Input price
$0.27 / M
— / M
Output price
$0.89 / M
— / M
Context window
131,072
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
DeepSeek-V3
Qwen3 VL 32B Thinking
Math
18.0#194
25.4#114
Finance
19.9#83
27.8#37
Legal
19.9#67
27.1#28
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for DeepSeek-V3 · 47 for Qwen3 VL 32B Thinking

6 shared

DeepSeek-V3 outperforms in 0 benchmarks, while Qwen3 VL 32B Thinking is better at 6 benchmarks (GPQA, IFEval, MMLU, MMLU-Pro, MMLU-Redux, SimpleQA).

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Thu Oct 08 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

638.0B diff

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

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

Context Window

Maximum input and output token capacity

Only DeepSeek-V3 specifies input context (131,072 tokens). Only DeepSeek-V3 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Thu Oct 08 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Qwen3 VL 32B Thinking supports multimodal inputs, whereas DeepSeek-V3 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

Text
Images
Audio
Video

Qwen3 VL 32B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), 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

MIT + Model License (Commercial use allowed)

Open weights

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3 was released on 2024-12-25, while Qwen3 VL 32B Thinking was released on 2025-09-22.

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

DeepSeek-V3

Dec 25, 2024

1.8 years ago

Qwen3 VL 32B Thinking

Sep 22, 2025

1.0 years ago

9mo 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?

Judge for yourself.

Run your own prompts against DeepSeek-V3 and Qwen3 VL 32B Thinking side-by-side, then vote on the output you prefer.

DeepSeek-V3
✓ Preferred
Qwen3 VL 32B Thinking
Open in Playground

FAQ

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

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

Qwen3 VL 32B Thinking leads the LLM Stats Score 23.5 to 15.6. DeepSeek-V3 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 capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek-V3 compare to Qwen3 VL 32B Thinking in benchmarks?

DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. 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 and Qwen3 VL 32B Thinking?

DeepSeek-V3 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 and Qwen3 VL 32B Thinking?

Key differences include LLM Stats Score (15.6 vs 23.5), multimodal support (no vs yes), licensing (MIT + Model License (Commercial use allowed) vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-V3 and Qwen3 VL 32B Thinking?

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

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