Model Comparison

Gemma 3n E2B vs Llama 3.2 3B Instruct

Both models are evenly matched across the benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Gemma 3n E2B outperforms in 1 benchmarks (HellaSwag), while Llama 3.2 3B Instruct is better at 1 benchmark (ARC-C).

Both models are evenly matched across the benchmarks.

Sat May 02 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
Sat May 02 2026 • llm-stats.com
Google
Gemma 3n E2B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Meta
Llama 3.2 3B Instruct
Input tokens$0.01
Output tokens$0.02
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

4.8B diff

Gemma 3n E2B has 4.8B more parameters than Llama 3.2 3B Instruct, making it 149.2% larger.

Google
Gemma 3n E2B
8.0Bparameters
Meta
Llama 3.2 3B Instruct
3.2Bparameters
8.0B
Gemma 3n E2B
3.2B
Llama 3.2 3B Instruct

Context Window

Maximum input and output token capacity

Only Llama 3.2 3B Instruct specifies input context (128,000 tokens). Only Llama 3.2 3B Instruct specifies output context (128,000 tokens).

Google
Gemma 3n E2B
Input- tokens
Output- tokens
Meta
Llama 3.2 3B Instruct
Input128,000 tokens
Output128,000 tokens
Sat May 02 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3n E2B supports multimodal inputs, whereas Llama 3.2 3B Instruct does not.

Gemma 3n E2B can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemma 3n E2B

Text
Images
Audio
Video

Llama 3.2 3B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Gemma 3n E2B is licensed under a proprietary license, while Llama 3.2 3B Instruct uses Llama 3.2 Community License.

License differences may affect how you can use these models in commercial or open-source projects.

Gemma 3n E2B

Proprietary

Closed source

Llama 3.2 3B Instruct

Llama 3.2 Community License

Open weights

Release Timeline

When each model was launched

Gemma 3n E2B was released on 2025-06-26, while Llama 3.2 3B Instruct was released on 2024-09-25.

Gemma 3n E2B is 9 months newer than Llama 3.2 3B Instruct.

Gemma 3n E2B

Jun 26, 2025

10 months ago

9mo newer
Llama 3.2 3B Instruct

Sep 25, 2024

1.6 years ago

Knowledge Cutoff

When training data ends

Gemma 3n E2B has a documented knowledge cutoff of 2024-06-01, while Llama 3.2 3B Instruct'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 Llama 3.2 3B Instruct's cutoff date.

Gemma 3n E2B

Jun 2024

Llama 3.2 3B Instruct

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Outputs Comparison

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Key Takeaways

Google
Supports multimodal inputs
Higher HellaSwag score (72.2% vs 69.8%)
Meta
Larger context window (128,000 tokens)
Has open weights
Higher ARC-C score (78.6% vs 51.7%)

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemma 3n E2B
Meta
Llama 3.2 3B Instruct

FAQ

Common questions about Gemma 3n E2B vs Llama 3.2 3B Instruct

Both models are evenly matched across the benchmarks. Gemma 3n E2B is made by Google and Llama 3.2 3B Instruct is made by Meta. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Gemma 3n E2B scores PIQA: 78.9%, BoolQ: 76.4%, ARC-E: 75.8%, HellaSwag: 72.2%, Winogrande: 66.8%. Llama 3.2 3B Instruct scores NIH/Multi-needle: 84.7%, ARC-C: 78.6%, GSM8k: 77.7%, IFEval: 77.4%, HellaSwag: 69.8%.
Gemma 3n E2B supports an unknown number of tokens and Llama 3.2 3B Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (yes vs no), licensing (Proprietary vs Llama 3.2 Community License). See the full comparison above for benchmark-by-benchmark results.
Gemma 3n E2B is developed by Google and Llama 3.2 3B Instruct is developed by Meta.