Model Comparison

DeepSeek-R1-0528 vs Gemma 3 1B

DeepSeek-R1-0528 significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

DeepSeek-R1-0528 outperforms in 4 benchmarks (GPQA, LiveCodeBench, MMLU-Pro, SimpleQA), while Gemma 3 1B is better at 0 benchmarks.

DeepSeek-R1-0528 significantly outperforms across most benchmarks.

Fri May 01 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
Fri May 01 2026 • llm-stats.com
DeepSeek
DeepSeek-R1-0528
Input tokens$0.50
Output tokens$2.15
Best providerDeepinfra
Google
Gemma 3 1B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

670.0B diff

DeepSeek-R1-0528 has 670.0B more parameters than Gemma 3 1B, making it 67000.0% larger.

DeepSeek
DeepSeek-R1-0528
671.0Bparameters
Google
Gemma 3 1B
1.0Bparameters
671.0B
DeepSeek-R1-0528
1.0B
Gemma 3 1B

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 3 1B
Input- tokens
Output- tokens
Fri May 01 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-R1-0528 is licensed under MIT, while Gemma 3 1B 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 3 1B

Gemma

Open weights

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while Gemma 3 1B was released on 2025-03-12.

DeepSeek-R1-0528 is 3 months newer than Gemma 3 1B.

DeepSeek-R1-0528

May 28, 2025

11 months ago

2mo newer
Gemma 3 1B

Mar 12, 2025

1.1 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

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Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Higher GPQA score (81.0% vs 19.2%)
Higher LiveCodeBench score (73.3% vs 1.9%)
Higher MMLU-Pro score (85.0% vs 14.7%)
Higher SimpleQA score (92.3% vs 2.2%)
Google

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1-0528
Google
Gemma 3 1B

FAQ

Common questions about DeepSeek-R1-0528 vs Gemma 3 1B

DeepSeek-R1-0528 significantly outperforms across most benchmarks. DeepSeek-R1-0528 is made by DeepSeek and Gemma 3 1B is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. Gemma 3 1B scores IFEval: 80.2%, GSM8k: 62.8%, Natural2Code: 56.0%, MATH: 48.0%, HumanEval: 41.5%.
DeepSeek-R1-0528 supports 131K tokens and Gemma 3 1B 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 (MIT vs Gemma). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-R1-0528 is developed by DeepSeek and Gemma 3 1B is developed by Google.