Model Comparison

DeepSeek-V3.2-Speciale vs Gemma 3n E2B Instructed LiteRT (Preview)

DeepSeek-V3.2-Speciale significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

DeepSeek-V3.2-Speciale outperforms in 1 benchmarks (AIME 2025), while Gemma 3n E2B Instructed LiteRT (Preview) is better at 0 benchmarks.

DeepSeek-V3.2-Speciale significantly outperforms across most benchmarks.

Sun May 31 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

683.1B diff

DeepSeek-V3.2-Speciale has 683.1B more parameters than Gemma 3n E2B Instructed LiteRT (Preview), making it 35763.9% larger.

DeepSeek
DeepSeek-V3.2-Speciale
685.0Bparameters
Google
Gemma 3n E2B Instructed LiteRT (Preview)
1.9Bparameters
685.0B
DeepSeek-V3.2-Speciale
1.9B
Gemma 3n E2B Instructed LiteRT (Preview)

Context Window

Maximum input and output token capacity

Only DeepSeek-V3.2-Speciale specifies input context (131,072 tokens). Only DeepSeek-V3.2-Speciale specifies output context (131,072 tokens).

DeepSeek
DeepSeek-V3.2-Speciale
Input131,072 tokens
Output131,072 tokens
Google
Gemma 3n E2B Instructed LiteRT (Preview)
Input- tokens
Output- tokens
Sun May 31 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3n E2B Instructed LiteRT (Preview) supports multimodal inputs, whereas DeepSeek-V3.2-Speciale 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-V3.2-Speciale

Text
Images
Audio
Video

Gemma 3n E2B Instructed LiteRT (Preview)

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2-Speciale 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-V3.2-Speciale

MIT

Open weights

Gemma 3n E2B Instructed LiteRT (Preview)

Gemma

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2-Speciale was released on 2025-12-01, while Gemma 3n E2B Instructed LiteRT (Preview) was released on 2025-05-20.

DeepSeek-V3.2-Speciale is 7 months newer than Gemma 3n E2B Instructed LiteRT (Preview).

DeepSeek-V3.2-Speciale

Dec 1, 2025

6 months ago

6mo newer
Gemma 3n E2B Instructed LiteRT (Preview)

May 20, 2025

1.0 years ago

Knowledge Cutoff

When training data ends

Gemma 3n E2B Instructed LiteRT (Preview) has a documented knowledge cutoff of 2024-06-01, while DeepSeek-V3.2-Speciale'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-V3.2-Speciale's cutoff date.

DeepSeek-V3.2-Speciale

—

Gemma 3n E2B Instructed LiteRT (Preview)

Jun 2024

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Higher AIME 2025 score (96.0% vs 6.7%)
Google
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2-Speciale
Google
Gemma 3n E2B Instructed LiteRT (Preview)

FAQ

Common questions about DeepSeek-V3.2-Speciale vs Gemma 3n E2B Instructed LiteRT (Preview).

Which is better, DeepSeek-V3.2-Speciale or Gemma 3n E2B Instructed LiteRT (Preview)?

DeepSeek-V3.2-Speciale significantly outperforms across most benchmarks. DeepSeek-V3.2-Speciale 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-V3.2-Speciale compare to Gemma 3n E2B Instructed LiteRT (Preview) in benchmarks?

DeepSeek-V3.2-Speciale scores HMMT 2025: 99.2%, AIME 2025: 96.0%, CodeForces: 90.0%, t2-bench: 80.3%, SWE-Bench Verified: 73.1%. 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-V3.2-Speciale and Gemma 3n E2B Instructed LiteRT (Preview)?

DeepSeek-V3.2-Speciale supports 131K 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-V3.2-Speciale 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-V3.2-Speciale and Gemma 3n E2B Instructed LiteRT (Preview)?

DeepSeek-V3.2-Speciale is developed by DeepSeek and Gemma 3n E2B Instructed LiteRT (Preview) is developed by Google.