Model Comparison

DeepSeek VL2 Tiny vs Qwen3 VL 4B Thinking

Qwen3 VL 4B Thinking significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

DeepSeek VL2 Tiny outperforms in 1 benchmarks (OCRBench), while Qwen3 VL 4B Thinking is better at 4 benchmarks (AI2D, MMBench-V1.1, MMStar, RealWorldQA).

Qwen3 VL 4B Thinking significantly outperforms across most benchmarks.

Tue Apr 21 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

Tue Apr 21 2026 • llm-stats.com

DeepSeek VL2 Tiny

Input tokens$0.00

Output tokens$0.00

Best providerUnknown Organization

Qwen3 VL 4B Thinking

Input tokens$0.10

Output tokens$1.00

Best providerDeepinfra

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Model Size

Parameter count comparison

1.0B diff

Qwen3 VL 4B Thinking has 1.0B more parameters than DeepSeek VL2 Tiny, making it 33.3% larger.

DeepSeek VL2 Tiny

3.0Bparameters

Qwen3 VL 4B Thinking

4.0Bparameters

3.0B

DeepSeek VL2 Tiny

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).

DeepSeek VL2 Tiny

Input- tokens

Output- tokens

Qwen3 VL 4B Thinking

Input262,144 tokens

Output262,144 tokens

Tue Apr 21 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both DeepSeek VL2 Tiny and Qwen3 VL 4B Thinking support multimodal inputs.

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

DeepSeek VL2 Tiny

Text

Images

Audio

Video

Qwen3 VL 4B Thinking

Text

Images

Audio

Video

License

Usage and distribution terms

DeepSeek VL2 Tiny is licensed under deepseek, 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.

DeepSeek VL2 Tiny

deepseek

Open weights

Qwen3 VL 4B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek VL2 Tiny was released on 2024-12-13, while Qwen3 VL 4B Thinking was released on 2025-09-22.

Qwen3 VL 4B Thinking is 9 months newer than DeepSeek VL2 Tiny.

DeepSeek VL2 Tiny

Dec 13, 2024

1.4 years ago

Qwen3 VL 4B Thinking

Sep 22, 2025

7 months ago

9mo 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

DeepSeek VL2 Tiny

View details

DeepSeek

Higher OCRBench score (80.9% vs 80.8%)

Qwen3 VL 4B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)

Higher AI2D score (84.9% vs 71.6%)

Higher MMBench-V1.1 score (86.7% vs 68.3%)

Higher MMStar score (73.2% vs 45.9%)

Higher RealWorldQA score (73.2% vs 64.2%)

Detailed Comparison

AI Model Comparison Table
Feature	DeepSeek VL2 Tiny	Qwen3 VL 4B Thinking

FAQ

Common questions about DeepSeek VL2 Tiny vs Qwen3 VL 4B Thinking

Qwen3 VL 4B Thinking significantly outperforms across most benchmarks. DeepSeek VL2 Tiny is made by DeepSeek 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.

DeepSeek VL2 Tiny scores DocVQA: 88.9%, ChartQA: 81.0%, OCRBench: 80.9%, TextVQA: 80.7%, AI2D: 71.6%. Qwen3 VL 4B Thinking scores DocVQAtest: 94.2%, ScreenSpot: 92.9%, MMBench-V1.1: 86.7%, MMLU-Redux: 86.0%, AI2D: 84.9%.

DeepSeek VL2 Tiny 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.

Key differences include licensing (deepseek vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

DeepSeek VL2 Tiny is developed by DeepSeek and Qwen3 VL 4B Thinking is developed by Alibaba Cloud / Qwen Team.

DeepSeek VL2 Tiny vs Qwen3 VL 4B Thinking

Performance Benchmarks

Arena Performance

Pricing Analysis

Model Size

Context Window

Input Capabilities

DeepSeek VL2 Tiny

Qwen3 VL 4B Thinking

License

Release Timeline

Knowledge Cutoff

Outputs Comparison

Key Takeaways

DeepSeek VL2 Tiny

Qwen3 VL 4B Thinking

Detailed Comparison

FAQ

Which is better, DeepSeek VL2 Tiny or Qwen3 VL 4B Thinking?

How does DeepSeek VL2 Tiny compare to Qwen3 VL 4B Thinking in benchmarks?

What are the context window sizes for DeepSeek VL2 Tiny and Qwen3 VL 4B Thinking?

What are the main differences between DeepSeek VL2 Tiny and Qwen3 VL 4B Thinking?

Who makes DeepSeek VL2 Tiny and Qwen3 VL 4B Thinking?