Model Comparison

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

Qwen3 VL 32B Instruct has a slight edge in benchmark performance.

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Performance Benchmarks

Comparative analysis across standard metrics

45 benchmarks

Qwen3 VL 30B A3B Thinking outperforms in 17 benchmarks (AIME 2025, CharadesSTA, GPQA, Hallusion Bench, Include, LiveCodeBench v6, MathVision, MMLU, MMLU-Pro, MMLU-ProX, MMLU-Redux, MuirBench, Multi-IF, OCRBench-V2 (zh), PolyMATH, SuperGPQA, WritingBench), while Qwen3 VL 32B Instruct is better at 27 benchmarks (AI2D, Arena-Hard v2, BFCL-v3, BLINK, CC-OCR, CharXiv-D, CharXiv-R, Creative Writing v3, DocVQAtest, ERQA, IFEval, InfoVQAtest, LiveBench 20241125, LVBench, MathVista-Mini, MLVU-M, MM-MT-Bench, MMMU-Pro, MMStar, MVBench, OCRBench, OCRBench-V2 (en), ODinW, OSWorld, RealWorldQA, ScreenSpot, ScreenSpot Pro).

Qwen3 VL 32B Instruct has a slight edge in benchmark performance.

Sun May 24 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

2.0B diff

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

Qwen3 VL 30B A3B Thinking

31.0Bparameters

Qwen3 VL 32B Instruct

33.0Bparameters

31.0B

Qwen3 VL 30B A3B Thinking

33.0B

Qwen3 VL 32B Instruct

Context Window

Maximum input and output token capacity

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

Qwen3 VL 30B A3B Thinking

Input131,072 tokens

Output32,768 tokens

Qwen3 VL 32B Instruct

Input- tokens

Output- tokens

Sun May 24 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

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

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

Qwen3 VL 30B A3B Thinking

Text

Images

Audio

Video

Qwen3 VL 32B Instruct

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 Thinking

Apache 2.0

Open weights

Qwen3 VL 32B Instruct

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 Thinking

Sep 22, 2025

8 months ago

Qwen3 VL 32B Instruct

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 30B A3B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (131,072 tokens)

Higher AIME 2025 score (83.1% vs 66.2%)

Higher CharadesSTA score (62.7% vs 61.2%)

Higher GPQA score (74.4% vs 68.9%)

Higher Hallusion Bench score (66.0% vs 63.8%)

Higher Include score (74.5% vs 74.0%)

Higher LiveCodeBench v6 score (64.2% vs 43.8%)

Higher MathVision score (65.7% vs 63.4%)

Higher MMLU score (87.6% vs 86.4%)

Higher MMLU-Pro score (80.5% vs 78.6%)

Higher MMLU-ProX score (76.1% vs 73.4%)

Higher MMLU-Redux score (90.9% vs 89.8%)

Higher MuirBench score (77.6% vs 72.8%)

Higher Multi-IF score (73.0% vs 72.0%)

Higher OCRBench-V2 (zh) score (60.4% vs 59.2%)

Higher PolyMATH score (51.7% vs 40.5%)

Higher SuperGPQA score (56.4% vs 54.6%)

Higher WritingBench score (85.2% vs 82.9%)

Qwen3 VL 32B Instruct

View details

Alibaba Cloud / Qwen Team

Higher AI2D score (89.5% vs 86.9%)

Higher Arena-Hard v2 score (64.7% vs 56.7%)

Higher BFCL-v3 score (70.2% vs 68.6%)

Higher BLINK score (67.3% vs 65.4%)

Higher CC-OCR score (80.3% vs 77.8%)

Higher CharXiv-D score (90.5% vs 86.9%)

Higher CharXiv-R score (62.8% vs 56.6%)

Higher Creative Writing v3 score (85.6% vs 82.5%)

Higher DocVQAtest score (96.9% vs 95.0%)

Higher ERQA score (48.8% vs 45.3%)

Higher IFEval score (84.7% vs 81.7%)

Higher InfoVQAtest score (87.0% vs 86.0%)

Higher LiveBench 20241125 score (72.2% vs 72.1%)

Higher LVBench score (63.8% vs 59.2%)

Higher MathVista-Mini score (83.8% vs 81.9%)

Higher MLVU-M score (82.1% vs 78.9%)

Higher MM-MT-Bench score (8.4% vs 7.9%)

Higher MMMU-Pro score (65.3% vs 63.0%)

Higher MMStar score (77.7% vs 75.5%)

Higher MVBench score (72.8% vs 72.0%)

Higher OCRBench score (89.5% vs 83.9%)

Higher OCRBench-V2 (en) score (67.4% vs 62.6%)

Higher ODinW score (46.6% vs 42.3%)

Higher OSWorld score (32.6% vs 30.6%)

Higher RealWorldQA score (79.0% vs 77.4%)

Higher ScreenSpot score (95.8% vs 94.7%)

Higher ScreenSpot Pro score (57.9% vs 57.3%)

Detailed Comparison

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

FAQ

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

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

Qwen3 VL 32B Instruct has a slight edge in benchmark performance. Qwen3 VL 30B A3B Thinking is made by Alibaba Cloud / Qwen Team 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 Qwen3 VL 30B A3B Thinking compare to Qwen3 VL 32B Instruct in benchmarks?

Qwen3 VL 30B A3B Thinking scores DocVQAtest: 95.0%, ScreenSpot: 94.7%, MMLU-Redux: 90.9%, MMBench-V1.1: 88.9%, MMLU: 87.6%. 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 Qwen3 VL 30B A3B Thinking and Qwen3 VL 32B Instruct?

Qwen3 VL 30B A3B 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.