Model Comparison

Qwen2.5 VL 7B 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

12 benchmarks

Qwen2.5 VL 7B Instruct outperforms in 3 benchmarks (CC-OCR, MVBench, OCRBench), while Qwen3 VL 4B Thinking is better at 9 benchmarks (CharadesSTA, Hallusion Bench, LVBench, MathVision, MathVista-Mini, MMMU-Pro, MMStar, ScreenSpot, ScreenSpot 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

4.3B diff

Qwen2.5 VL 7B Instruct has 4.3B more parameters than Qwen3 VL 4B Thinking, making it 107.3% larger.

Qwen2.5 VL 7B Instruct

8.3Bparameters

Qwen3 VL 4B Thinking

4.0Bparameters

8.3B

Qwen2.5 VL 7B 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).

Qwen2.5 VL 7B 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

Both Qwen2.5 VL 7B Instruct and Qwen3 VL 4B Thinking support multimodal inputs.

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

Qwen2.5 VL 7B Instruct

Text

Images

Audio

Video

Qwen3 VL 4B 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.

Qwen2.5 VL 7B Instruct

Apache 2.0

Open weights

Qwen3 VL 4B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Qwen2.5 VL 7B Instruct was released on 2025-01-26, while Qwen3 VL 4B Thinking was released on 2025-09-22.

Qwen3 VL 4B Thinking is 8 months newer than Qwen2.5 VL 7B Instruct.

Qwen2.5 VL 7B Instruct

Jan 26, 2025

1.3 years ago

Qwen3 VL 4B Thinking

Sep 22, 2025

8 months ago

7mo 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

Qwen2.5 VL 7B Instruct

View details

Alibaba Cloud / Qwen Team

Higher CC-OCR score (77.8% vs 73.8%)

Higher MVBench score (69.6% vs 69.3%)

Higher OCRBench score (86.4% vs 80.8%)

Qwen3 VL 4B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Higher CharadesSTA score (59.0% vs 43.6%)

Higher Hallusion Bench score (64.1% vs 52.9%)

Higher LVBench score (53.5% vs 45.3%)

Higher MathVision score (60.0% vs 25.1%)

Higher MathVista-Mini score (79.5% vs 68.2%)

Higher MMMU-Pro score (57.0% vs 38.3%)

Higher MMStar score (73.2% vs 63.9%)

Higher ScreenSpot score (92.9% vs 84.7%)

Higher ScreenSpot Pro score (49.2% vs 29.0%)

Detailed Comparison

AI Model Comparison Table
Feature	Qwen2.5 VL 7B Instruct	Qwen3 VL 4B Thinking

FAQ

Common questions about Qwen2.5 VL 7B Instruct vs Qwen3 VL 4B Thinking.

Which is better, Qwen2.5 VL 7B Instruct or Qwen3 VL 4B Thinking?

Qwen3 VL 4B Thinking shows notably better performance in the majority of benchmarks. Qwen2.5 VL 7B 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 Qwen2.5 VL 7B Instruct compare to Qwen3 VL 4B Thinking in benchmarks?

Qwen2.5 VL 7B Instruct scores DocVQA: 95.7%, Android Control Low_EM: 91.4%, MobileMiniWob++_SR: 91.4%, ChartQA: 87.3%, OCRBench: 86.4%. 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 Qwen2.5 VL 7B Instruct and Qwen3 VL 4B Thinking?

Qwen2.5 VL 7B 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.