Model Comparison
Qwen2-VL-72B-Instruct vs Qwen3 VL 4B ThinkingWhich is better in 2026?
Qwen2-VL-72B-Instruct shows notably better performance in the majority of benchmarks.
Verdict: Qwen2-VL-72B-Instruct vs Qwen3 VL 4B Thinking — which is better?
Qwen2-VL-72B-Instruct (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.
Qwen2-VL-72B-Instruct outperforms in 5 benchmarks (DocVQAtest, InfoVQAtest, MVBench, OCRBench, RealWorldQA), while Qwen3 VL 4B Thinking is better at 2 benchmarks (MathVista-Mini, MMMU-Pro). Qwen2-VL-72B-Instruct shows notably better performance in the majority of benchmarks.
Choose Qwen2-VL-72B-Instruct if…
- you want the strongest raw capability — it leads on 5 of 7 shared benchmarks
Choose Qwen3 VL 4B Thinking if…
- you want the most recent training data — it shipped Sep 2025
Performance Benchmarks
Comparative analysis across standard metrics
Qwen2-VL-72B-Instruct outperforms in 5 benchmarks (DocVQAtest, InfoVQAtest, MVBench, OCRBench, RealWorldQA), while Qwen3 VL 4B Thinking is better at 2 benchmarks (MathVista-Mini, MMMU-Pro).
Qwen2-VL-72B-Instruct shows notably better performance in the majority of benchmarks.
Arena Performance
Human preference votes
Model Size
Parameter count comparison
Qwen2-VL-72B-Instruct has 69.4B more parameters than Qwen3 VL 4B Thinking, making it 1735.0% larger.
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).
Input Capabilities
Supported data types and modalities
Both Qwen2-VL-72B-Instruct and Qwen3 VL 4B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Qwen2-VL-72B-Instruct
Qwen3 VL 4B Thinking
License
Usage and distribution terms
Qwen2-VL-72B-Instruct is licensed under tongyi-qianwen, while Qwen3 VL 4B Thinking uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
tongyi-qianwen
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Qwen2-VL-72B-Instruct was released on 2024-08-29, while Qwen3 VL 4B Thinking was released on 2025-09-22.
Qwen3 VL 4B Thinking is 13 months newer than Qwen2-VL-72B-Instruct.
Aug 29, 2024
1.8 years ago
Sep 22, 2025
8 months ago
1.1yr newerKnowledge Cutoff
When training data ends
Qwen2-VL-72B-Instruct has a documented knowledge cutoff of 2023-06-30, while Qwen3 VL 4B Thinking's cutoff date is not specified.
We can confirm Qwen2-VL-72B-Instruct's training data extends to 2023-06-30, but cannot make a direct comparison without Qwen3 VL 4B Thinking's cutoff date.
Jun 2023
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Outputs Comparison
Key Takeaways
Qwen2-VL-72B-Instruct
View detailsAlibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about Qwen2-VL-72B-Instruct vs Qwen3 VL 4B Thinking.