Model Comparison

Qwen3 VL 30B A3B Instruct vs Qwen3 VL 32B Thinking

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

45 benchmarks

Qwen3 VL 30B A3B Instruct outperforms in 4 benchmarks (CharadesSTA, Creative Writing v3, OCRBench, ScreenSpot Pro), while Qwen3 VL 32B Thinking is better at 41 benchmarks (AI2D, AIME 2025, Arena-Hard v2, BFCL-v3, BLINK, CharXiv-D, CharXiv-R, 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-V2 (en), OCRBench-V2 (zh), OSWorld, PolyMATH, RealWorldQA, ScreenSpot, SimpleQA, SuperGPQA, VideoMMMU, WritingBench).

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Sun May 10 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

2.0B diff

Qwen3 VL 32B Thinking has 2.0B more parameters than Qwen3 VL 30B A3B Instruct, making it 6.5% larger.

Qwen3 VL 30B A3B Instruct

31.0Bparameters

Qwen3 VL 32B Thinking

33.0Bparameters

31.0B

Qwen3 VL 30B A3B Instruct

33.0B

Qwen3 VL 32B Thinking

Context Window

Maximum input and output token capacity

Only Qwen3 VL 30B A3B Instruct specifies input context (131,072 tokens). Only Qwen3 VL 30B A3B Instruct specifies output context (32,768 tokens).

Qwen3 VL 30B A3B Instruct

Input131,072 tokens

Output32,768 tokens

Qwen3 VL 32B Thinking

Input- tokens

Output- tokens

Sun May 10 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Qwen3 VL 30B A3B Instruct and Qwen3 VL 32B Thinking support multimodal inputs.

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

Qwen3 VL 30B A3B Instruct

Text

Images

Audio

Video

Qwen3 VL 32B 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 30B A3B Instruct

Apache 2.0

Open weights

Qwen3 VL 32B 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 30B A3B Instruct

Sep 22, 2025

7 months ago

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

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Qwen3 VL 30B A3B Instruct

View details

Alibaba Cloud / Qwen Team

Larger context window (131,072 tokens)

Higher CharadesSTA score (63.5% vs 62.8%)

Higher Creative Writing v3 score (84.6% vs 83.3%)

Higher OCRBench score (90.3% vs 85.5%)

Higher ScreenSpot Pro score (60.5% vs 57.1%)

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

Higher AI2D score (88.9% vs 85.0%)

Higher AIME 2025 score (83.7% vs 69.3%)

Higher Arena-Hard v2 score (60.5% vs 58.5%)

Higher BFCL-v3 score (71.7% vs 66.3%)

Higher BLINK score (68.5% vs 67.7%)

Higher CharXiv-D score (90.2% vs 85.5%)

Higher CharXiv-R score (65.2% vs 48.9%)

Higher DocVQAtest score (96.1% vs 95.0%)

Higher ERQA score (52.3% vs 43.0%)

Higher GPQA score (73.1% vs 70.4%)

Higher Hallusion Bench score (67.4% vs 61.5%)

Higher IFEval score (87.8% vs 85.8%)

Higher Include score (76.3% vs 71.6%)

Higher InfoVQAtest score (89.2% vs 82.0%)

Higher LiveBench 20241125 score (74.7% vs 65.4%)

Higher LiveCodeBench v6 score (65.6% vs 42.6%)

Higher LVBench score (62.6% vs 62.5%)

Higher MathVision score (70.2% vs 60.2%)

Higher MathVista-Mini score (85.9% vs 80.1%)

Higher MMBench-V1.1 score (90.8% vs 87.0%)

Higher MMLU score (88.7% vs 85.0%)

Higher MMLU-Pro score (82.1% vs 77.8%)

Higher MMLU-ProX score (77.2% vs 70.9%)

Higher MMLU-Redux score (91.9% vs 88.4%)

Higher MM-MT-Bench score (83.0% vs 8.1%)

Higher MMMU-Pro score (68.1% vs 60.4%)

Higher MMMU (val) score (78.1% vs 74.2%)

Higher MMStar score (79.4% vs 72.1%)

Higher MuirBench score (80.3% vs 62.9%)

Higher Multi-IF score (78.0% vs 66.1%)

Higher MVBench score (73.2% vs 72.3%)

Higher OCRBench-V2 (en) score (68.4% vs 63.2%)

Higher OCRBench-V2 (zh) score (62.1% vs 57.8%)

Higher OSWorld score (41.0% vs 30.3%)

Higher PolyMATH score (52.0% vs 44.3%)

Higher RealWorldQA score (78.4% vs 73.7%)

Higher ScreenSpot score (95.7% vs 94.7%)

Higher SimpleQA score (55.4% vs 27.0%)

Higher SuperGPQA score (59.0% vs 53.1%)

Higher VideoMMMU score (79.0% vs 68.7%)

Higher WritingBench score (86.2% vs 82.6%)

Detailed Comparison

AI Model Comparison Table
Feature	Qwen3 VL 30B A3B Instruct	Qwen3 VL 32B Thinking

FAQ

Common questions about Qwen3 VL 30B A3B Instruct vs Qwen3 VL 32B Thinking.

Which is better, Qwen3 VL 30B A3B Instruct or Qwen3 VL 32B Thinking?

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks. Qwen3 VL 30B A3B Instruct is made by Alibaba Cloud / Qwen Team 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 Qwen3 VL 30B A3B Instruct compare to Qwen3 VL 32B Thinking in benchmarks?

Qwen3 VL 30B A3B Instruct scores DocVQAtest: 95.0%, ScreenSpot: 94.7%, OCRBench: 90.3%, MMLU-Redux: 88.4%, MMBench-V1.1: 87.0%. 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 Qwen3 VL 30B A3B Instruct and Qwen3 VL 32B Thinking?

Qwen3 VL 30B A3B Instruct 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.