Model Comparison

Qwen2 72B Instruct vs Qwen3 VL 4B Thinking

Qwen3 VL 4B Thinking shows notably better performance in the majority of benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

Qwen2 72B Instruct outperforms in 1 benchmarks (MMLU), while Qwen3 VL 4B Thinking is better at 2 benchmarks (GPQA, MMLU-Pro).

Qwen3 VL 4B Thinking shows notably better performance in the majority of benchmarks.

Mon May 25 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

68.0B diff

Qwen2 72B Instruct has 68.0B more parameters than Qwen3 VL 4B Thinking, making it 1700.0% larger.

Qwen2 72B Instruct

72.0Bparameters

Qwen3 VL 4B Thinking

4.0Bparameters

72.0B

Qwen2 72B Instruct

4.0B

Qwen3 VL 4B Thinking

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).

Qwen2 72B Instruct

Input- tokens

Output- tokens

Qwen3 VL 4B Thinking

Input262,144 tokens

Output262,144 tokens

Mon May 25 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen3 VL 4B Thinking supports multimodal inputs, whereas Qwen2 72B Instruct does not.

Qwen3 VL 4B Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.

Qwen2 72B Instruct

Text

Images

Audio

Video

Qwen3 VL 4B Thinking

Text

Images

Audio

Video

License

Usage and distribution terms

Qwen2 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.

Qwen2 72B Instruct

tongyi-qianwen

Open weights

Qwen3 VL 4B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Qwen2 72B Instruct was released on 2024-07-23, while Qwen3 VL 4B Thinking was released on 2025-09-22.

Qwen3 VL 4B Thinking is 14 months newer than Qwen2 72B Instruct.

Qwen2 72B Instruct

Jul 23, 2024

1.8 years ago

Qwen3 VL 4B Thinking

Sep 22, 2025

8 months ago

1.2yr newer

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

Qwen2 72B Instruct

View details

Alibaba Cloud / Qwen Team

Higher MMLU score (82.3% vs 81.5%)

Qwen3 VL 4B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Supports multimodal inputs

Higher GPQA score (64.1% vs 42.4%)

Higher MMLU-Pro score (73.6% vs 64.4%)

Detailed Comparison

AI Model Comparison Table
Feature	Qwen2 72B Instruct	Qwen3 VL 4B Thinking

FAQ

Common questions about Qwen2 72B Instruct vs Qwen3 VL 4B Thinking.

Which is better, Qwen2 72B Instruct or Qwen3 VL 4B Thinking?

Qwen3 VL 4B Thinking shows notably better performance in the majority of benchmarks. Qwen2 72B Instruct is made by Alibaba Cloud / Qwen Team and Qwen3 VL 4B 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 Qwen2 72B Instruct compare to Qwen3 VL 4B Thinking in benchmarks?

Qwen2 72B Instruct scores GSM8k: 91.1%, CMMLU: 90.1%, HellaSwag: 87.6%, HumanEval: 86.0%, Winogrande: 85.1%. Qwen3 VL 4B Thinking scores DocVQAtest: 94.2%, ScreenSpot: 92.9%, MMBench-V1.1: 86.7%, MMLU-Redux: 86.0%, AI2D: 84.9%.

What are the context window sizes for Qwen2 72B Instruct and Qwen3 VL 4B Thinking?

Qwen2 72B Instruct supports an unknown number of tokens and Qwen3 VL 4B Thinking supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Qwen2 72B Instruct and Qwen3 VL 4B Thinking?

Key differences include multimodal support (no vs yes), licensing (tongyi-qianwen vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.