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Gemma 3n E4B vs Qwen3 VL 4B Thinking

Qwen3 VL 4B Thinking leads the LLM Stats Score 12.9 to -6.2.

Google · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Qwen3 VL 4B Thinking leads the overall LLM Stats Score 12.9 to -6.2, ranking #253 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Gemma 3n E4B

  • you are already invested in the Google ecosystem

Choose Qwen3 VL 4B Thinking

  • overall performance matters — it scores 12.9 and ranks #253 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Sep 2025
  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

Gemma 3n E4B
Google
Qwen3 VL 4B Thinking
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
-6.2
#365
12.9
#253
Reasoning
-6.4
#357
14.0
#235
Cost, coverage & limits
Benchmark wins
—
—
Input price
— / M
$0.10 / M
Output price
— / M
$1.00 / M
Context window
—
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Gemma 3n E4B
Qwen3 VL 4B Thinking
Math
-2.8#313
15.6#217
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

11 reported for Gemma 3n E4B · 48 for Qwen3 VL 4B Thinking

No common benchmarks found

Gemma 3n E4B and Qwen3 VL 4B Thinkingdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

4.0B diff

Gemma 3n E4B has 4.0B more parameters than Qwen3 VL 4B Thinking, making it 100.0% larger.

Google
Gemma 3n E4B
8.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
4.0Bparameters
8.0B
Gemma 3n E4B
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 E4B
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
Input262,144 tokens
Output262,144 tokens
Tue Sep 22 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both Gemma 3n E4B and Qwen3 VL 4B Thinking support multimodal inputs.

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

Gemma 3n E4B

Text
Images
Audio
Video

Qwen3 VL 4B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Gemma 3n E4B 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 E4B

Proprietary

Closed source

Qwen3 VL 4B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Gemma 3n E4B 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 E4B.

Gemma 3n E4B

Jun 26, 2025

1.2 years ago

Qwen3 VL 4B Thinking

Sep 22, 2025

1.0 years ago

2mo newer

Knowledge Cutoff

When training data ends

Gemma 3n E4B 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 E4B's training data extends to 2024-06-01, but cannot make a direct comparison without Qwen3 VL 4B Thinking's cutoff date.

Gemma 3n E4B

Jun 2024

Qwen3 VL 4B Thinking

—

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against Gemma 3n E4B and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.

Gemma 3n E4B
✓ Preferred
Qwen3 VL 4B Thinking
Open in Playground

FAQ

Common questions about Gemma 3n E4B vs Qwen3 VL 4B Thinking.

Which is better, Gemma 3n E4B or Qwen3 VL 4B Thinking?

Qwen3 VL 4B Thinking leads the LLM Stats Score 12.9 to -6.2. Gemma 3n E4B is made by Google and Qwen3 VL 4B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Gemma 3n E4B compare to Qwen3 VL 4B Thinking in benchmarks?

Gemma 3n E4B scores ARC-E: 81.6%, BoolQ: 81.6%, PIQA: 81.0%, HellaSwag: 78.6%, Winogrande: 71.7%. 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 E4B and Qwen3 VL 4B Thinking?

Gemma 3n E4B 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 E4B and Qwen3 VL 4B Thinking?

Key differences include LLM Stats Score (-6.2 vs 12.9), licensing (Proprietary vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemma 3n E4B and Qwen3 VL 4B Thinking?

Gemma 3n E4B is developed by Google and Qwen3 VL 4B Thinking is developed by Alibaba Cloud / Qwen Team.

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