Model Comparison

DeepSeek-R1-0528 vs Gemma 2 27B

Comparing DeepSeek-R1-0528 and Gemma 2 27B 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 2 27B don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

643.8B diff

DeepSeek-R1-0528 has 643.8B more parameters than Gemma 2 27B, making it 2366.9% larger.

DeepSeek
DeepSeek-R1-0528
671.0Bparameters
Google
Gemma 2 27B
27.2Bparameters
671.0B
DeepSeek-R1-0528
27.2B
Gemma 2 27B

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 2 27B
Input- tokens
Output- tokens
Tue May 26 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-R1-0528 is licensed under MIT, while Gemma 2 27B uses Gemma.

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

DeepSeek-R1-0528

MIT

Open weights

Gemma 2 27B

Gemma

Open weights

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while Gemma 2 27B was released on 2024-06-27.

DeepSeek-R1-0528 is 11 months newer than Gemma 2 27B.

DeepSeek-R1-0528

May 28, 2025

12 months ago

11mo newer
Gemma 2 27B

Jun 27, 2024

1.9 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Google

No standout differentiators in the data we have for this pair.

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1-0528
Google
Gemma 2 27B

FAQ

Common questions about DeepSeek-R1-0528 vs Gemma 2 27B.

Which is better, DeepSeek-R1-0528 or Gemma 2 27B?

DeepSeek-R1-0528 (DeepSeek) and Gemma 2 27B (Google) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does DeepSeek-R1-0528 compare to Gemma 2 27B in benchmarks?

DeepSeek-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. Gemma 2 27B scores ARC-E: 88.6%, HellaSwag: 86.4%, BoolQ: 84.8%, TriviaQA: 83.7%, Winogrande: 83.7%.

What are the context window sizes for DeepSeek-R1-0528 and Gemma 2 27B?

DeepSeek-R1-0528 supports 131K tokens and Gemma 2 27B 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-R1-0528 and Gemma 2 27B?

Key differences include licensing (MIT vs Gemma). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek-R1-0528 and Gemma 2 27B?

DeepSeek-R1-0528 is developed by DeepSeek and Gemma 2 27B is developed by Google.