Model Comparison

DeepSeek-V3.1 vs Gemma 3n E2B Instructed LiteRT (Preview)

DeepSeek-V3.1 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek-V3.1 outperforms in 4 benchmarks (AIME 2025, GPQA, LiveCodeBench, MMLU-Pro), while Gemma 3n E2B Instructed LiteRT (Preview) is better at 0 benchmarks.

DeepSeek-V3.1 significantly outperforms across most benchmarks.

Tue May 12 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

669.1B diff

DeepSeek-V3.1 has 669.1B more parameters than Gemma 3n E2B Instructed LiteRT (Preview), making it 35030.9% larger.

DeepSeek
DeepSeek-V3.1
671.0Bparameters
Google
Gemma 3n E2B Instructed LiteRT (Preview)
1.9Bparameters
671.0B
DeepSeek-V3.1
1.9B
Gemma 3n E2B Instructed LiteRT (Preview)

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.1 specifies input context (163,840 tokens). Only DeepSeek-V3.1 specifies output context (163,840 tokens).

DeepSeek
DeepSeek-V3.1
Input163,840 tokens
Output163,840 tokens
Google
Gemma 3n E2B Instructed LiteRT (Preview)
Input- tokens
Output- tokens
Tue May 12 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3n E2B Instructed LiteRT (Preview) supports multimodal inputs, whereas DeepSeek-V3.1 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.

DeepSeek-V3.1

Text
Images
Audio
Video

Gemma 3n E2B Instructed LiteRT (Preview)

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.1 is licensed under MIT, while Gemma 3n E2B Instructed LiteRT (Preview) uses Gemma.

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

DeepSeek-V3.1

MIT

Open weights

Gemma 3n E2B Instructed LiteRT (Preview)

Gemma

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.1 was released on 2025-01-10, while Gemma 3n E2B Instructed LiteRT (Preview) was released on 2025-05-20.

Gemma 3n E2B Instructed LiteRT (Preview) is 4 months newer than DeepSeek-V3.1.

DeepSeek-V3.1

Jan 10, 2025

1.3 years ago

Gemma 3n E2B Instructed LiteRT (Preview)

May 20, 2025

11 months ago

4mo newer

Knowledge Cutoff

When training data ends

Gemma 3n E2B Instructed LiteRT (Preview) has a documented knowledge cutoff of 2024-06-01, while DeepSeek-V3.1'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 DeepSeek-V3.1's cutoff date.

DeepSeek-V3.1

—

Gemma 3n E2B Instructed LiteRT (Preview)

Jun 2024

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (163,840 tokens)
Higher AIME 2025 score (49.8% vs 6.7%)
Higher GPQA score (74.9% vs 24.8%)
Higher LiveCodeBench score (56.4% vs 13.2%)
Higher MMLU-Pro score (83.7% vs 40.5%)
Google
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.1
Google
Gemma 3n E2B Instructed LiteRT (Preview)

FAQ

Common questions about DeepSeek-V3.1 vs Gemma 3n E2B Instructed LiteRT (Preview).

Which is better, DeepSeek-V3.1 or Gemma 3n E2B Instructed LiteRT (Preview)?

DeepSeek-V3.1 significantly outperforms across most benchmarks. DeepSeek-V3.1 is made by DeepSeek and Gemma 3n E2B Instructed LiteRT (Preview) is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does DeepSeek-V3.1 compare to Gemma 3n E2B Instructed LiteRT (Preview) in benchmarks?

DeepSeek-V3.1 scores SimpleQA: 93.4%, MMLU-Redux: 91.8%, MMLU-Pro: 83.7%, GPQA: 74.9%, CodeForces: 69.7%. Gemma 3n E2B Instructed LiteRT (Preview) scores PIQA: 78.9%, BoolQ: 76.4%, ARC-E: 75.8%, HellaSwag: 72.2%, Winogrande: 66.8%.

What are the context window sizes for DeepSeek-V3.1 and Gemma 3n E2B Instructed LiteRT (Preview)?

DeepSeek-V3.1 supports 164K tokens and Gemma 3n E2B 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.

What are the main differences between DeepSeek-V3.1 and Gemma 3n E2B Instructed LiteRT (Preview)?

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

Who makes DeepSeek-V3.1 and Gemma 3n E2B Instructed LiteRT (Preview)?

DeepSeek-V3.1 is developed by DeepSeek and Gemma 3n E2B Instructed LiteRT (Preview) is developed by Google.