Model Comparison

Gemini 3 Pro vs Gemma 3n E2B InstructedWhich is better in 2026?

Gemini 3 Pro significantly outperforms across most benchmarks.

Verdict: Gemini 3 Pro vs Gemma 3n E2B Instructed — which is better?

Gemini 3 Pro (by Google) and Gemma 3n E2B Instructed (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 3 Pro outperforms in 2 benchmarks (AIME 2025, GPQA), while Gemma 3n E2B Instructed is better at 0 benchmarks. Gemini 3 Pro significantly outperforms across most benchmarks.

Choose Gemini 3 Pro if…

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
  • you want the most recent training data — it shipped Nov 2025

Choose Gemma 3n E2B Instructed if…

  • you are already invested in the Google ecosystem

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Gemini 3 Pro outperforms in 2 benchmarks (AIME 2025, GPQA), while Gemma 3n E2B Instructed is better at 0 benchmarks.

Gemini 3 Pro significantly outperforms across most benchmarks.

Wed Jun 10 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only Gemini 3 Pro specifies input context (1,048,576 tokens). Only Gemini 3 Pro specifies output context (65,536 tokens).

Google
Gemini 3 Pro
Input1,048,576 tokens
Output65,536 tokens
Google
Gemma 3n E2B Instructed
Input- tokens
Output- tokens
Wed Jun 10 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemini 3 Pro and Gemma 3n E2B Instructed support multimodal inputs.

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

Gemini 3 Pro

Text
Images
Audio
Video

Gemma 3n E2B Instructed

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 3 Pro

Proprietary

Closed source

Gemma 3n E2B Instructed

Proprietary

Closed source

Release Timeline

When each model was launched

Gemini 3 Pro was released on 2025-11-18, while Gemma 3n E2B Instructed was released on 2025-06-26.

Gemini 3 Pro is 5 months newer than Gemma 3n E2B Instructed.

Gemini 3 Pro

Nov 18, 2025

6 months ago

4mo newer
Gemma 3n E2B Instructed

Jun 26, 2025

11 months ago

Knowledge Cutoff

When training data ends

Gemini 3 Pro has a knowledge cutoff of 2025-01-31, while Gemma 3n E2B Instructed has a cutoff of 2024-06-01.

Gemini 3 Pro has more recent training data (up to 2025-01-31), making it potentially better informed about events through that date compared to Gemma 3n E2B Instructed (2024-06-01).

Gemini 3 Pro

Jan 2025

7 mo newer
Gemma 3n E2B Instructed

Jun 2024

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Key Takeaways

Larger context window (1,048,576 tokens)
Higher AIME 2025 score (100.0% vs 6.7%)
Higher GPQA score (91.9% vs 24.8%)

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

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 3 Pro
Google
Gemma 3n E2B Instructed

FAQ

Common questions about Gemini 3 Pro vs Gemma 3n E2B Instructed.

Which is better, Gemini 3 Pro or Gemma 3n E2B Instructed?

Gemini 3 Pro significantly outperforms across most benchmarks. Gemini 3 Pro is made by Google and Gemma 3n E2B Instructed is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Gemini 3 Pro compare to Gemma 3n E2B Instructed in benchmarks?

Gemini 3 Pro scores AIME 2025: 100.0%, Vending-Bench 2: 100.0%, Global PIQA: 93.4%, GPQA: 91.9%, MMMLU: 91.8%. Gemma 3n E2B Instructed scores HumanEval: 66.5%, MMLU: 60.1%, Global-MMLU-Lite: 59.0%, MBPP: 56.6%, Global-MMLU: 55.1%.

What are the context window sizes for Gemini 3 Pro and Gemma 3n E2B Instructed?

Gemini 3 Pro supports 1.0M tokens and Gemma 3n E2B Instructed supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.