Model Comparison

Gemma 3n E2B Instructed LiteRT (Preview) vs Llama 3.2 3B InstructWhich is better in 2026?

Llama 3.2 3B Instruct significantly outperforms across most benchmarks.

Verdict: Gemma 3n E2B Instructed LiteRT (Preview) vs Llama 3.2 3B Instruct — which is better?

Gemma 3n E2B Instructed LiteRT (Preview) (by Google) and Llama 3.2 3B Instruct (by Meta) 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.

Gemma 3n E2B Instructed LiteRT (Preview) outperforms in 1 benchmarks (HellaSwag), while Llama 3.2 3B Instruct is better at 4 benchmarks (ARC-C, GPQA, MGSM, MMLU). Llama 3.2 3B Instruct significantly outperforms across most benchmarks.

Choose Gemma 3n E2B Instructed LiteRT (Preview) if…

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

Choose Llama 3.2 3B Instruct if…

  • you want the strongest raw capability — it leads on 4 of 5 shared benchmarks

Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

Gemma 3n E2B Instructed LiteRT (Preview) outperforms in 1 benchmarks (HellaSwag), while Llama 3.2 3B Instruct is better at 4 benchmarks (ARC-C, GPQA, MGSM, MMLU).

Llama 3.2 3B Instruct significantly outperforms across most benchmarks.

Tue Jul 21 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

1.3B diff

Llama 3.2 3B Instruct has 1.3B more parameters than Gemma 3n E2B Instructed LiteRT (Preview), making it 68.1% larger.

Google
Gemma 3n E2B Instructed LiteRT (Preview)
1.9Bparameters
Meta
Llama 3.2 3B Instruct
3.2Bparameters
1.9B
Gemma 3n E2B Instructed LiteRT (Preview)
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 Instructed LiteRT (Preview)
Input- tokens
Output- tokens
Meta
Llama 3.2 3B Instruct
Input128,000 tokens
Output128,000 tokens
Tue Jul 21 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3n E2B Instructed LiteRT (Preview) supports multimodal inputs, whereas Llama 3.2 3B Instruct does not.

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

Gemma 3n E2B Instructed LiteRT (Preview)

Text
Images
Audio
Video

Llama 3.2 3B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Gemma 3n E2B Instructed LiteRT (Preview) is licensed under Gemma, 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 Instructed LiteRT (Preview)

Gemma

Open weights

Llama 3.2 3B Instruct

Llama 3.2 Community License

Open weights

Release Timeline

When each model was launched

Gemma 3n E2B Instructed LiteRT (Preview) was released on 2025-05-20, while Llama 3.2 3B Instruct was released on 2024-09-25.

Gemma 3n E2B Instructed LiteRT (Preview) is 8 months newer than Llama 3.2 3B Instruct.

Gemma 3n E2B Instructed LiteRT (Preview)

May 20, 2025

1.2 years ago

7mo newer
Llama 3.2 3B Instruct

Sep 25, 2024

1.8 years ago

Knowledge Cutoff

When training data ends

Gemma 3n E2B Instructed LiteRT (Preview) 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 Instructed LiteRT (Preview)'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 Instructed LiteRT (Preview)

Jun 2024

Llama 3.2 3B Instruct

—

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Google
Supports multimodal inputs
Higher HellaSwag score (72.2% vs 69.8%)
Meta
Larger context window (128,000 tokens)
Higher ARC-C score (78.6% vs 51.7%)
Higher GPQA score (32.8% vs 24.8%)
Higher MGSM score (58.2% vs 53.1%)
Higher MMLU score (63.4% vs 60.1%)

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against Gemma 3n E2B Instructed LiteRT (Preview) and Llama 3.2 3B Instruct side-by-side, then vote on the output you prefer.

Gemma 3n E2B Instructed LiteRT (Preview)
✓ Preferred
Llama 3.2 3B Instruct
Open in Playground
AI Model Comparison Table
Feature
Google
Gemma 3n E2B Instructed LiteRT (Preview)
Meta
Llama 3.2 3B Instruct

FAQ

Common questions about Gemma 3n E2B Instructed LiteRT (Preview) vs Llama 3.2 3B Instruct.

Which is better, Gemma 3n E2B Instructed LiteRT (Preview) or Llama 3.2 3B Instruct?

Llama 3.2 3B Instruct significantly outperforms across most benchmarks. Gemma 3n E2B Instructed LiteRT (Preview) 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.

How does Gemma 3n E2B Instructed LiteRT (Preview) compare to Llama 3.2 3B Instruct in benchmarks?

Gemma 3n E2B Instructed LiteRT (Preview) 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%.

What are the context window sizes for Gemma 3n E2B Instructed LiteRT (Preview) and Llama 3.2 3B Instruct?

Gemma 3n E2B Instructed LiteRT (Preview) 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.

What are the main differences between Gemma 3n E2B Instructed LiteRT (Preview) and Llama 3.2 3B Instruct?

Key differences include multimodal support (yes vs no), licensing (Gemma vs Llama 3.2 Community License). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemma 3n E2B Instructed LiteRT (Preview) and Llama 3.2 3B Instruct?

Gemma 3n E2B Instructed LiteRT (Preview) is developed by Google and Llama 3.2 3B Instruct is developed by Meta.

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