Model Comparison

Qwen3 VL 32B Thinking vs Qwen3 VL 8B Thinking

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Performance Benchmarks

Comparative analysis across standard metrics

45 benchmarks

Qwen3 VL 32B Thinking outperforms in 44 benchmarks (AI2D, AIME 2025, Arena-Hard v2, BFCL-v3, 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, SimpleQA, SuperGPQA, VideoMMMU, WritingBench), while Qwen3 VL 8B Thinking is better at 1 benchmark (BLINK).

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Thu Apr 09 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
Thu Apr 09 2026 • llm-stats.com
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Alibaba Cloud / Qwen Team
Qwen3 VL 8B Thinking
Input tokens$0.18
Output tokens$2.09
Best providerDeepinfra
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Model Size

Parameter count comparison

24.0B diff

Qwen3 VL 32B Thinking has 24.0B more parameters than Qwen3 VL 8B Thinking, making it 266.7% larger.

Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
33.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 8B Thinking
9.0Bparameters
33.0B
Qwen3 VL 32B Thinking
9.0B
Qwen3 VL 8B Thinking

Context Window

Maximum input and output token capacity

Only Qwen3 VL 8B Thinking specifies input context (262,144 tokens). Only Qwen3 VL 8B Thinking specifies output context (262,144 tokens).

Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 8B Thinking
Input262,144 tokens
Output262,144 tokens
Thu Apr 09 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Qwen3 VL 32B Thinking and Qwen3 VL 8B 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 8B 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 8B 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

6 months ago

Qwen3 VL 8B Thinking

Sep 22, 2025

6 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

Alibaba Cloud / Qwen Team

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 80.3%)
Higher Arena-Hard v2 score (60.5% vs 51.1%)
Higher BFCL-v3 score (71.7% vs 63.0%)
Higher CharadesSTA score (62.8% vs 59.9%)
Higher CharXiv-D score (90.2% vs 85.9%)
Higher CharXiv-R score (65.2% vs 53.0%)
Higher Creative Writing v3 score (83.3% vs 82.4%)
Higher DocVQAtest score (96.1% vs 95.3%)
Higher ERQA score (52.3% vs 46.8%)
Higher GPQA score (73.1% vs 69.9%)
Higher Hallusion Bench score (67.4% vs 65.4%)
Higher IFEval score (87.8% vs 83.2%)
Higher Include score (76.3% vs 69.5%)
Higher InfoVQAtest score (89.2% vs 86.0%)
Higher LiveBench 20241125 score (74.7% vs 69.8%)
Higher LiveCodeBench v6 score (65.6% vs 58.6%)
Higher LVBench score (62.6% vs 55.8%)
Higher MathVision score (70.2% vs 62.7%)
Higher MathVista-Mini score (85.9% vs 81.4%)
Higher MMBench-V1.1 score (90.8% vs 87.5%)
Higher MMLU score (88.7% vs 85.2%)
Higher MMLU-Pro score (82.1% vs 77.3%)
Higher MMLU-ProX score (77.2% vs 70.7%)
Higher MMLU-Redux score (91.9% vs 88.8%)
Higher MM-MT-Bench score (83.0% vs 8.0%)
Higher MMMU-Pro score (68.1% vs 60.4%)
Higher MMMU (val) score (78.1% vs 74.1%)
Higher MMStar score (79.4% vs 75.3%)
Higher MuirBench score (80.3% vs 76.8%)
Higher Multi-IF score (78.0% vs 75.1%)
Higher MVBench score (73.2% vs 69.0%)
Higher OCRBench score (85.5% vs 81.9%)
Higher OCRBench-V2 (en) score (68.4% vs 63.9%)
Higher OCRBench-V2 (zh) score (62.1% vs 59.2%)
Higher OSWorld score (41.0% vs 33.9%)
Higher PolyMATH score (52.0% vs 47.5%)
Higher RealWorldQA score (78.4% vs 73.5%)
Higher ScreenSpot score (95.7% vs 93.6%)
Higher ScreenSpot Pro score (57.1% vs 46.6%)
Higher SimpleQA score (55.4% vs 49.6%)
Higher SuperGPQA score (59.0% vs 51.2%)
Higher VideoMMMU score (79.0% vs 72.8%)
Higher WritingBench score (86.2% vs 85.5%)
Alibaba Cloud / Qwen Team

Qwen3 VL 8B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)
Higher BLINK score (68.7% vs 68.5%)

Detailed Comparison

AI Model Comparison Table
Feature
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Alibaba Cloud / Qwen Team
Qwen3 VL 8B Thinking

FAQ

Common questions about Qwen3 VL 32B Thinking vs Qwen3 VL 8B Thinking

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks. Qwen3 VL 32B Thinking is made by Alibaba Cloud / Qwen Team and Qwen3 VL 8B 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 Thinking scores DocVQAtest: 96.1%, ScreenSpot: 95.7%, MMLU-Redux: 91.9%, MMBench-V1.1: 90.8%, CharXiv-D: 90.2%. Qwen3 VL 8B Thinking scores DocVQAtest: 95.3%, ScreenSpot: 93.6%, MMLU-Redux: 88.8%, MMBench-V1.1: 87.5%, InfoVQAtest: 86.0%.
Qwen3 VL 32B Thinking supports an unknown number of tokens and Qwen3 VL 8B Thinking supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.