Model Comparison

Gemma 3n E2B vs Qwen3 VL 4B ThinkingWhich is better in 2026?

Comparing Gemma 3n E2B and Qwen3 VL 4B Thinking across benchmarks, pricing, and capabilities.

Verdict: Gemma 3n E2B vs Qwen3 VL 4B Thinking — which is better?

Gemma 3n E2B (by Google) 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.

Choose Gemma 3n E2B if…

  • you are already invested in the Google ecosystem

Choose Qwen3 VL 4B Thinking if…

  • you want the most recent training data — it shipped Sep 2025
  • you need open weights you can self-host or fine-tune

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Gemma 3n E2B and Qwen3 VL 4B Thinkingdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

4.0B diff

Gemma 3n E2B has 4.0B more parameters than Qwen3 VL 4B Thinking, making it 100.0% larger.

Google
Gemma 3n E2B
8.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
4.0Bparameters
8.0B
Gemma 3n E2B
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).

Google
Gemma 3n E2B
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
Input262,144 tokens
Output262,144 tokens
Fri Jun 12 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemma 3n E2B and Qwen3 VL 4B Thinking support multimodal inputs.

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

Gemma 3n E2B

Text
Images
Audio
Video

Qwen3 VL 4B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Gemma 3n E2B is licensed under a proprietary license, 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.

Gemma 3n E2B

Proprietary

Closed source

Qwen3 VL 4B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Gemma 3n E2B was released on 2025-06-26, while Qwen3 VL 4B Thinking was released on 2025-09-22.

Qwen3 VL 4B Thinking is 3 months newer than Gemma 3n E2B.

Gemma 3n E2B

Jun 26, 2025

11 months ago

Qwen3 VL 4B Thinking

Sep 22, 2025

8 months ago

2mo newer

Knowledge Cutoff

When training data ends

Gemma 3n E2B has a documented knowledge cutoff of 2024-06-01, while Qwen3 VL 4B Thinking's cutoff date is not specified.

We can confirm Gemma 3n E2B's training data extends to 2024-06-01, but cannot make a direct comparison without Qwen3 VL 4B Thinking's cutoff date.

Gemma 3n E2B

Jun 2024

Qwen3 VL 4B Thinking

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Key Takeaways

No standout differentiators in the data we have for this pair.

Alibaba Cloud / Qwen Team

Qwen3 VL 4B Thinking

View details

Alibaba Cloud / Qwen Team

Larger context window (262,144 tokens)
Has open weights

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemma 3n E2B
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking

FAQ

Common questions about Gemma 3n E2B vs Qwen3 VL 4B Thinking.

Which is better, Gemma 3n E2B or Qwen3 VL 4B Thinking?

Gemma 3n E2B (Google) and Qwen3 VL 4B Thinking (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Gemma 3n E2B compare to Qwen3 VL 4B Thinking in benchmarks?

Gemma 3n E2B scores PIQA: 78.9%, BoolQ: 76.4%, ARC-E: 75.8%, HellaSwag: 72.2%, Winogrande: 66.8%. 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 Gemma 3n E2B and Qwen3 VL 4B Thinking?

Gemma 3n E2B 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 Gemma 3n E2B and Qwen3 VL 4B Thinking?

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

Who makes Gemma 3n E2B and Qwen3 VL 4B Thinking?

Gemma 3n E2B is developed by Google and Qwen3 VL 4B Thinking is developed by Alibaba Cloud / Qwen Team.