Model Comparison

DeepSeek-R1-0528 vs Gemma 3n E2B

Comparing DeepSeek-R1-0528 and Gemma 3n E2B across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1-0528 and Gemma 3n E2B don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Fri May 01 2026 • llm-stats.com
DeepSeek
DeepSeek-R1-0528
Input tokens$0.50
Output tokens$2.15
Best providerDeepinfra
Google
Gemma 3n E2B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

663.0B diff

DeepSeek-R1-0528 has 663.0B more parameters than Gemma 3n E2B, making it 8287.5% larger.

DeepSeek
DeepSeek-R1-0528
671.0Bparameters
Google
Gemma 3n E2B
8.0Bparameters
671.0B
DeepSeek-R1-0528
8.0B
Gemma 3n E2B

Context Window

Maximum input and output token capacity

Only DeepSeek-R1-0528 specifies input context (131,072 tokens). Only DeepSeek-R1-0528 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1-0528
Input131,072 tokens
Output131,072 tokens
Google
Gemma 3n E2B
Input- tokens
Output- tokens
Fri May 01 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3n E2B supports multimodal inputs, whereas DeepSeek-R1-0528 does not.

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

DeepSeek-R1-0528

Text
Images
Audio
Video

Gemma 3n E2B

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-R1-0528 is licensed under MIT, while Gemma 3n E2B uses a proprietary license.

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

DeepSeek-R1-0528

MIT

Open weights

Gemma 3n E2B

Proprietary

Closed source

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while Gemma 3n E2B was released on 2025-06-26.

Gemma 3n E2B is 1 month newer than DeepSeek-R1-0528.

DeepSeek-R1-0528

May 28, 2025

11 months ago

Gemma 3n E2B

Jun 26, 2025

10 months ago

4w newer

Knowledge Cutoff

When training data ends

Gemma 3n E2B has a documented knowledge cutoff of 2024-06-01, while DeepSeek-R1-0528'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 DeepSeek-R1-0528's cutoff date.

DeepSeek-R1-0528

—

Gemma 3n E2B

Jun 2024

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Has open weights
Google
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1-0528
Google
Gemma 3n E2B

FAQ

Common questions about DeepSeek-R1-0528 vs Gemma 3n E2B

DeepSeek-R1-0528 (DeepSeek) and Gemma 3n E2B (Google) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
DeepSeek-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. Gemma 3n E2B scores PIQA: 78.9%, BoolQ: 76.4%, ARC-E: 75.8%, HellaSwag: 72.2%, Winogrande: 66.8%.
DeepSeek-R1-0528 supports 131K tokens and Gemma 3n E2B 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 multimodal support (no vs yes), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-R1-0528 is developed by DeepSeek and Gemma 3n E2B is developed by Google.