Model Comparison

Qwen3 VL 32B Thinking vs Qwen3 VL 4B ThinkingWhich is better in 2026?

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Verdict: Qwen3 VL 32B Thinking vs Qwen3 VL 4B Thinking — which is better?

Qwen3 VL 32B Thinking (by Alibaba Cloud / Qwen Team) and Qwen3 VL 4B 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.

Qwen3 VL 32B Thinking outperforms in 44 benchmarks (AI2D, AIME 2025, Arena-Hard v2, BFCL-v3, BLINK, CharadesSTA, CharXiv-D, CharXiv-R, Creative Writing v3, DocVQAtest, ERQA, GPQA, Hallusion Bench, IFEval, Include, InfoVQAtest, LiveBench 20241125, LiveCodeBench v6, LVBench, MathVision, MathVista-Mini, MMBench-V1.1, MMLU, MMLU-Pro, MMLU-ProX, MMLU-Redux, MM-MT-Bench, MMMU-Pro, MMMU (val), MMStar, MuirBench, Multi-IF, MVBench, OCRBench, OCRBench-V2 (en), OCRBench-V2 (zh), OSWorld, PolyMATH, RealWorldQA, ScreenSpot, ScreenSpot Pro, SuperGPQA, VideoMMMU, WritingBench), while Qwen3 VL 4B Thinking is better at 0 benchmarks. Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Choose Qwen3 VL 32B Thinking if…

you want the strongest raw capability — it leads on 44 of 44 shared benchmarks

Choose Qwen3 VL 4B Thinking if…

you want predictable pricing at $0.10/M input and $1.00/M output

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

44 benchmarks

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Sat Jun 13 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

29.0B diff

Qwen3 VL 32B Thinking has 29.0B more parameters than Qwen3 VL 4B Thinking, making it 725.0% larger.

Qwen3 VL 32B Thinking

33.0Bparameters

Qwen3 VL 4B Thinking

4.0Bparameters

33.0B

Qwen3 VL 32B Thinking

4.0B

Qwen3 VL 4B Thinking

Context Window

Maximum input and output token capacity

Only Qwen3 VL 4B Thinking specifies input context (262,144 tokens). Only Qwen3 VL 4B Thinking specifies output context (262,144 tokens).

Qwen3 VL 32B Thinking

Input- tokens

Output- tokens

Qwen3 VL 4B Thinking

Input262,144 tokens

Output262,144 tokens

Sat Jun 13 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Qwen3 VL 32B Thinking and Qwen3 VL 4B Thinking support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Qwen3 VL 32B Thinking

Text

Images

Audio

Video

Qwen3 VL 4B Thinking

Text

Images

Audio

Video

License

Usage and distribution terms

Both models are licensed under Apache 2.0.

Both models share the same licensing terms, providing consistent usage rights.

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Qwen3 VL 4B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Both models were released on 2025-09-22.

They likely represent similar generations of model development.

Qwen3 VL 32B Thinking

Sep 22, 2025

8 months ago

Qwen3 VL 4B Thinking

Sep 22, 2025

8 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

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

Higher AI2D score (88.9% vs 84.9%)

Higher AIME 2025 score (83.7% vs 74.5%)

Higher Arena-Hard v2 score (60.5% vs 36.8%)

Higher BFCL-v3 score (71.7% vs 67.3%)

Higher BLINK score (68.5% vs 63.4%)

Higher CharadesSTA score (62.8% vs 59.0%)

Higher CharXiv-D score (90.2% vs 83.9%)

Higher CharXiv-R score (65.2% vs 50.3%)

Higher Creative Writing v3 score (83.3% vs 76.1%)

Higher DocVQAtest score (96.1% vs 94.2%)

Higher ERQA score (52.3% vs 47.3%)

Higher GPQA score (73.1% vs 64.1%)

Higher Hallusion Bench score (67.4% vs 64.1%)

Higher IFEval score (87.8% vs 82.6%)

Higher Include score (76.3% vs 64.6%)

Higher InfoVQAtest score (89.2% vs 83.0%)

Higher LiveBench 20241125 score (74.7% vs 68.4%)

Higher LiveCodeBench v6 score (65.6% vs 51.3%)

Higher LVBench score (62.6% vs 53.5%)

Higher MathVision score (70.2% vs 60.0%)

Higher MathVista-Mini score (85.9% vs 79.5%)

Higher MMBench-V1.1 score (90.8% vs 86.7%)

Higher MMLU score (88.7% vs 81.5%)

Higher MMLU-Pro score (82.1% vs 73.6%)

Higher MMLU-ProX score (77.2% vs 65.0%)

Higher MMLU-Redux score (91.9% vs 86.0%)

Higher MM-MT-Bench score (83.0% vs 7.7%)

Higher MMMU-Pro score (68.1% vs 57.0%)

Higher MMMU (val) score (78.1% vs 70.8%)

Higher MMStar score (79.4% vs 73.2%)

Higher MuirBench score (80.3% vs 75.0%)

Higher Multi-IF score (78.0% vs 73.6%)

Higher MVBench score (73.2% vs 69.3%)

Higher OCRBench score (85.5% vs 80.8%)

Higher OCRBench-V2 (en) score (68.4% vs 61.8%)

Higher OCRBench-V2 (zh) score (62.1% vs 55.8%)

Higher OSWorld score (41.0% vs 31.4%)

Higher PolyMATH score (52.0% vs 44.6%)

Higher RealWorldQA score (78.4% vs 73.2%)

Higher ScreenSpot score (95.7% vs 92.9%)

Higher ScreenSpot Pro score (57.1% vs 49.2%)

Higher SuperGPQA score (59.0% vs 46.8%)

Higher VideoMMMU score (79.0% vs 69.4%)

Higher WritingBench score (86.2% vs 84.0%)

Qwen3 VL 4B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Detailed Comparison

AI Model Comparison Table
Feature	Qwen3 VL 32B Thinking	Qwen3 VL 4B Thinking

FAQ

Common questions about Qwen3 VL 32B Thinking vs Qwen3 VL 4B Thinking.

Which is better, Qwen3 VL 32B Thinking or Qwen3 VL 4B Thinking?

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks. Qwen3 VL 32B Thinking is made by Alibaba Cloud / Qwen Team and Qwen3 VL 4B 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 Qwen3 VL 32B Thinking compare to Qwen3 VL 4B 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%. Qwen3 VL 4B Thinking scores DocVQAtest: 94.2%, ScreenSpot: 92.9%, MMBench-V1.1: 86.7%, MMLU-Redux: 86.0%, AI2D: 84.9%.

What are the context window sizes for Qwen3 VL 32B Thinking and Qwen3 VL 4B Thinking?

Qwen3 VL 32B Thinking supports an unknown number of tokens and Qwen3 VL 4B Thinking supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

Qwen3 VL 32B Thinking vs Qwen3 VL 4B ThinkingWhich is better in 2026?

Verdict: Qwen3 VL 32B Thinking vs Qwen3 VL 4B Thinking — which is better?

Choose Qwen3 VL 32B Thinking if…

Choose Qwen3 VL 4B Thinking if…

Performance Benchmarks

Arena Performance

Model Size

Context Window

Input Capabilities

Qwen3 VL 32B Thinking

Qwen3 VL 4B Thinking

License

Release Timeline

Knowledge Cutoff

Outputs Comparison

Key Takeaways

Qwen3 VL 32B Thinking

Qwen3 VL 4B Thinking

Detailed Comparison

FAQ

Which is better, Qwen3 VL 32B Thinking or Qwen3 VL 4B Thinking?

How does Qwen3 VL 32B Thinking compare to Qwen3 VL 4B Thinking in benchmarks?

What are the context window sizes for Qwen3 VL 32B Thinking and Qwen3 VL 4B Thinking?

More Qwen3 VL 32B Thinking comparisons

More Qwen3 VL 4B Thinking comparisons

Qwen3 VL 32B Thinking vs Qwen3 VL 4B ThinkingWhich is better in 2026?

Verdict: Qwen3 VL 32B Thinking vs Qwen3 VL 4B Thinking — which is better?

Choose Qwen3 VL 32B Thinking if…

Choose Qwen3 VL 4B Thinking if…

Performance Benchmarks

Arena Performance

Model Size

Context Window

Input Capabilities

Qwen3 VL 32B Thinking

Qwen3 VL 4B Thinking

License

Release Timeline

Knowledge Cutoff

Outputs Comparison

Key Takeaways

Qwen3 VL 32B Thinking

Qwen3 VL 4B Thinking

Detailed Comparison

Which is better, Qwen3 VL 32B Thinking or Qwen3 VL 4B Thinking?

How does Qwen3 VL 32B Thinking compare to Qwen3 VL 4B Thinking in benchmarks?

What are the context window sizes for Qwen3 VL 32B Thinking and Qwen3 VL 4B Thinking?

Related comparisons

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