Model Comparison

Qwen3 VL 32B Instruct vs Qwen3 VL 32B Thinking

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

42 benchmarks

Qwen3 VL 32B Instruct outperforms in 10 benchmarks (AI2D, Arena-Hard v2, CharXiv-D, Creative Writing v3, DocVQAtest, LVBench, OCRBench, RealWorldQA, ScreenSpot, ScreenSpot Pro), while Qwen3 VL 32B Thinking is better at 32 benchmarks (AIME 2025, BFCL-v3, BLINK, CharadesSTA, CharXiv-R, ERQA, GPQA, Hallusion Bench, IFEval, Include, InfoVQAtest, LiveBench 20241125, LiveCodeBench v6, MathVision, MathVista-Mini, 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, SuperGPQA, WritingBench).

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Fri May 01 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers

Fri May 01 2026 • llm-stats.com

Qwen3 VL 32B Instruct

Input tokens$0.00

Output tokens$0.00

Best providerUnknown Organization

Qwen3 VL 32B Thinking

Input tokens$0.00

Output tokens$0.00

Best providerUnknown Organization

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Model Size

Parameter count comparison

0.0M diff

Qwen3 VL 32B Thinking has 0.0B more parameters than Qwen3 VL 32B Instruct, making it 0.0% larger.

Qwen3 VL 32B Instruct

33.0Bparameters

Qwen3 VL 32B Thinking

33.0Bparameters

33.0B

Qwen3 VL 32B Instruct

33.0B

Qwen3 VL 32B Thinking

Input Capabilities

Supported data types and modalities

Both Qwen3 VL 32B 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 32B 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 32B 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 32B 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

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Key Takeaways

Qwen3 VL 32B Instruct

View details

Alibaba Cloud / Qwen Team

Higher AI2D score (89.5% vs 88.9%)

Higher Arena-Hard v2 score (64.7% vs 60.5%)

Higher CharXiv-D score (90.5% vs 90.2%)

Higher Creative Writing v3 score (85.6% vs 83.3%)

Higher DocVQAtest score (96.9% vs 96.1%)

Higher LVBench score (63.8% vs 62.6%)

Higher OCRBench score (89.5% vs 85.5%)

Higher RealWorldQA score (79.0% vs 78.4%)

Higher ScreenSpot score (95.8% vs 95.7%)

Higher ScreenSpot Pro score (57.9% vs 57.1%)

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

Higher AIME 2025 score (83.7% vs 66.2%)

Higher BFCL-v3 score (71.7% vs 70.2%)

Higher BLINK score (68.5% vs 67.3%)

Higher CharadesSTA score (62.8% vs 61.2%)

Higher CharXiv-R score (65.2% vs 62.8%)

Higher ERQA score (52.3% vs 48.8%)

Higher GPQA score (73.1% vs 68.9%)

Higher Hallusion Bench score (67.4% vs 63.8%)

Higher IFEval score (87.8% vs 84.7%)

Higher Include score (76.3% vs 74.0%)

Higher InfoVQAtest score (89.2% vs 87.0%)

Higher LiveBench 20241125 score (74.7% vs 72.2%)

Higher LiveCodeBench v6 score (65.6% vs 43.8%)

Higher MathVision score (70.2% vs 63.4%)

Higher MathVista-Mini score (85.9% vs 83.8%)

Higher MMLU score (88.7% vs 86.4%)

Higher MMLU-Pro score (82.1% vs 78.6%)

Higher MMLU-ProX score (77.2% vs 73.4%)

Higher MMLU-Redux score (91.9% vs 89.8%)

Higher MM-MT-Bench score (83.0% vs 8.4%)

Higher MMMU-Pro score (68.1% vs 65.3%)

Higher MMMU (val) score (78.1% vs 76.0%)

Higher MMStar score (79.4% vs 77.7%)

Higher MuirBench score (80.3% vs 72.8%)

Higher Multi-IF score (78.0% vs 72.0%)

Higher MVBench score (73.2% vs 72.8%)

Higher OCRBench-V2 (en) score (68.4% vs 67.4%)

Higher OCRBench-V2 (zh) score (62.1% vs 59.2%)

Higher OSWorld score (41.0% vs 32.6%)

Higher PolyMATH score (52.0% vs 40.5%)

Higher SuperGPQA score (59.0% vs 54.6%)

Higher WritingBench score (86.2% vs 82.9%)

Detailed Comparison

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

FAQ

Common questions about Qwen3 VL 32B Instruct vs Qwen3 VL 32B Thinking

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks. Qwen3 VL 32B 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.

Qwen3 VL 32B Instruct scores DocVQAtest: 96.9%, ScreenSpot: 95.8%, CharXiv-D: 90.5%, MMLU-Redux: 89.8%, AI2D: 89.5%. Qwen3 VL 32B Thinking scores DocVQAtest: 96.1%, ScreenSpot: 95.7%, MMLU-Redux: 91.9%, MMBench-V1.1: 90.8%, CharXiv-D: 90.2%.

Qwen3 VL 32B Instruct vs Qwen3 VL 32B Thinking

Performance Benchmarks

Arena Performance

Pricing Analysis

Model Size

Input Capabilities

Qwen3 VL 32B Instruct

Qwen3 VL 32B Thinking

License

Release Timeline

Knowledge Cutoff

Outputs Comparison

Key Takeaways

Qwen3 VL 32B Instruct

Qwen3 VL 32B Thinking

Detailed Comparison

FAQ

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

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