Model Comparison

Gemini 1.5 Pro vs Gemma 3n E4BWhich is better in 2026?

Gemini 1.5 Pro significantly outperforms across most benchmarks.

Verdict: Gemini 1.5 Pro vs Gemma 3n E4B — which is better?

Gemini 1.5 Pro (by Google) and Gemma 3n E4B (by Google) 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.

Gemini 1.5 Pro outperforms in 3 benchmarks (BIG-Bench Hard, DROP, HellaSwag), while Gemma 3n E4B is better at 0 benchmarks. Gemini 1.5 Pro significantly outperforms across most benchmarks.

Choose Gemini 1.5 Pro if…

  • you want the strongest raw capability — it leads on 3 of 3 shared benchmarks

Choose Gemma 3n E4B if…

  • you want the most recent training data — it shipped Jun 2025

Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

Gemini 1.5 Pro outperforms in 3 benchmarks (BIG-Bench Hard, DROP, HellaSwag), while Gemma 3n E4B is better at 0 benchmarks.

Gemini 1.5 Pro significantly outperforms across most benchmarks.

Sat Jul 25 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only Gemini 1.5 Pro specifies input context (2,097,152 tokens). Only Gemini 1.5 Pro specifies output context (8,192 tokens).

Google
Gemini 1.5 Pro
Input2,097,152 tokens
Output8,192 tokens
Google
Gemma 3n E4B
Input- tokens
Output- tokens
Sat Jul 25 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemini 1.5 Pro and Gemma 3n E4B support multimodal inputs.

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

Gemini 1.5 Pro

Text
Images
Audio
Video

Gemma 3n E4B

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

Gemini 1.5 Pro

Proprietary

Closed source

Gemma 3n E4B

Proprietary

Closed source

Release Timeline

When each model was launched

Gemini 1.5 Pro was released on 2024-05-01, while Gemma 3n E4B was released on 2025-06-26.

Gemma 3n E4B is 14 months newer than Gemini 1.5 Pro.

Gemini 1.5 Pro

May 1, 2024

2.2 years ago

Gemma 3n E4B

Jun 26, 2025

1.1 years ago

1.2yr newer

Knowledge Cutoff

When training data ends

Gemini 1.5 Pro has a knowledge cutoff of 2023-11-01, while Gemma 3n E4B has a cutoff of 2024-06-01.

Gemma 3n E4B has more recent training data (up to 2024-06-01), making it potentially better informed about events through that date compared to Gemini 1.5 Pro (2023-11-01).

Gemini 1.5 Pro

Nov 2023

Gemma 3n E4B

Jun 2024

7 mo newer

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Google
Larger context window (2,097,152 tokens)
Higher BIG-Bench Hard score (89.2% vs 52.9%)
Higher DROP score (74.9% vs 60.8%)
Higher HellaSwag score (93.3% vs 78.6%)
Google

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

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against Gemini 1.5 Pro and Gemma 3n E4B side-by-side, then vote on the output you prefer.

Gemini 1.5 Pro
✓ Preferred
Gemma 3n E4B
Open in Playground
AI Model Comparison Table
Feature
Google
Gemini 1.5 Pro
Google
Gemma 3n E4B

FAQ

Common questions about Gemini 1.5 Pro vs Gemma 3n E4B.

Which is better, Gemini 1.5 Pro or Gemma 3n E4B?

Gemini 1.5 Pro significantly outperforms across most benchmarks. Gemini 1.5 Pro is made by Google and Gemma 3n E4B is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Gemini 1.5 Pro compare to Gemma 3n E4B in benchmarks?

Gemini 1.5 Pro scores XSTest: 98.8%, FLEURS: 93.3%, HellaSwag: 93.3%, GSM8k: 90.8%, BIG-Bench Hard: 89.2%. Gemma 3n E4B scores ARC-E: 81.6%, BoolQ: 81.6%, PIQA: 81.0%, HellaSwag: 78.6%, Winogrande: 71.7%.

What are the context window sizes for Gemini 1.5 Pro and Gemma 3n E4B?

Gemini 1.5 Pro supports 2.1M tokens and Gemma 3n E4B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

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