Model Comparison

Gemini 2.5 Flash vs Gemma 3n E4B Instructed LiteRT Preview

Gemini 2.5 Flash significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

Gemini 2.5 Flash outperforms in 4 benchmarks (AIME 2025, Global-MMLU-Lite, GPQA, LiveCodeBench v5), while Gemma 3n E4B Instructed LiteRT Preview is better at 0 benchmarks.

Gemini 2.5 Flash significantly outperforms across most benchmarks.

Thu Apr 30 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
Thu Apr 30 2026 • llm-stats.com
Google
Gemini 2.5 Flash
Input tokens$0.30
Output tokens$2.50
Best providerGoogle
Google
Gemma 3n E4B Instructed LiteRT Preview
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Context Window

Maximum input and output token capacity

Only Gemini 2.5 Flash specifies input context (1,048,576 tokens). Only Gemini 2.5 Flash specifies output context (65,536 tokens).

Google
Gemini 2.5 Flash
Input1,048,576 tokens
Output65,536 tokens
Google
Gemma 3n E4B Instructed LiteRT Preview
Input- tokens
Output- tokens
Thu Apr 30 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemini 2.5 Flash and Gemma 3n E4B Instructed LiteRT Preview support multimodal inputs.

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

Gemini 2.5 Flash

Text
Images
Audio
Video

Gemma 3n E4B Instructed LiteRT Preview

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 2.5 Flash is licensed under a proprietary license, while Gemma 3n E4B Instructed LiteRT Preview uses Gemma.

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

Gemini 2.5 Flash

Proprietary

Closed source

Gemma 3n E4B Instructed LiteRT Preview

Gemma

Open weights

Release Timeline

When each model was launched

Both models were released on 2025-05-20.

They likely represent similar generations of model development.

Gemini 2.5 Flash

May 20, 2025

11 months ago

Gemma 3n E4B Instructed LiteRT Preview

May 20, 2025

11 months ago

Knowledge Cutoff

When training data ends

Gemini 2.5 Flash has a knowledge cutoff of 2025-01-31, while Gemma 3n E4B Instructed LiteRT Preview has a cutoff of 2024-06-01.

Gemini 2.5 Flash has more recent training data (up to 2025-01-31), making it potentially better informed about events through that date compared to Gemma 3n E4B Instructed LiteRT Preview (2024-06-01).

Gemini 2.5 Flash

Jan 2025

7 mo newer
Gemma 3n E4B Instructed LiteRT Preview

Jun 2024

Outputs Comparison

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Key Takeaways

Google
Larger context window (1,048,576 tokens)
Higher AIME 2025 score (72.0% vs 11.6%)
Higher Global-MMLU-Lite score (88.4% vs 64.5%)
Higher GPQA score (82.8% vs 23.7%)
Higher LiveCodeBench v5 score (63.9% vs 25.7%)
Google
Has open weights
GoogleGemini 2.5 Flash
GoogleGemma 3n E4B Instructed LiteRT Preview
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Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 2.5 Flash
Google
Gemma 3n E4B Instructed LiteRT Preview

FAQ

Common questions about Gemini 2.5 Flash vs Gemma 3n E4B Instructed LiteRT Preview

Gemini 2.5 Flash significantly outperforms across most benchmarks. Gemini 2.5 Flash is made by Google and Gemma 3n E4B Instructed LiteRT Preview is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Gemini 2.5 Flash scores Global-MMLU-Lite: 88.4%, AIME 2024: 88.0%, FACTS Grounding: 85.3%, GPQA: 82.8%, MMMU: 79.7%. Gemma 3n E4B Instructed LiteRT Preview scores ARC-E: 81.6%, BoolQ: 81.6%, PIQA: 81.0%, HellaSwag: 78.6%, HumanEval: 75.0%.
Gemini 2.5 Flash supports 1.0M tokens and Gemma 3n E4B Instructed LiteRT Preview supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include licensing (Proprietary vs Gemma). See the full comparison above for benchmark-by-benchmark results.