Model Comparison
DeepSeek-R1-0528 vs Gemma 3 27BWhich is better in 2026?
DeepSeek-R1-0528 significantly outperforms across most benchmarks. Gemma 3 27B is 7.3x cheaper per token.
Verdict: DeepSeek-R1-0528 vs Gemma 3 27B — which is better?
DeepSeek-R1-0528 (by DeepSeek) and Gemma 3 27B (by Google) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
DeepSeek-R1-0528 outperforms in 4 benchmarks (GPQA, LiveCodeBench, MMLU-Pro, SimpleQA), while Gemma 3 27B is better at 0 benchmarks. DeepSeek-R1-0528 significantly outperforms across most benchmarks.
On price, Gemma 3 27B is roughly 7.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Choose DeepSeek-R1-0528 if…
- you want the strongest raw capability — it leads on 4 of 4 shared benchmarks
- you want the most recent training data — it shipped May 2025
Choose Gemma 3 27B if…
- cost matters — it's about 7.3x cheaper per token
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-R1-0528 outperforms in 4 benchmarks (GPQA, LiveCodeBench, MMLU-Pro, SimpleQA), while Gemma 3 27B is better at 0 benchmarks.
DeepSeek-R1-0528 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-R1-0528 ($0.50/1M tokens) is 5.0x more expensive than Gemma 3 27B ($0.10/1M tokens).
For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 10.7x more expensive than Gemma 3 27B ($0.20/1M tokens).
In conclusion, DeepSeek-R1-0528 is more expensive than Gemma 3 27B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1-0528 has 644.0B more parameters than Gemma 3 27B, making it 2385.2% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 131,072 tokens. Both models can generate responses up to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Gemma 3 27B supports multimodal inputs, whereas DeepSeek-R1-0528 does not.
Gemma 3 27B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1-0528
Gemma 3 27B
License
Usage and distribution terms
DeepSeek-R1-0528 is licensed under MIT, while Gemma 3 27B uses Gemma.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Gemma
Open weights
Release Timeline
When each model was launched
DeepSeek-R1-0528 was released on 2025-05-28, while Gemma 3 27B was released on 2025-03-12.
DeepSeek-R1-0528 is 3 months newer than Gemma 3 27B.
May 28, 2025
1.0 years ago
2mo newerMar 12, 2025
1.3 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-R1-0528 is available from DeepInfra, DeepSeek, Novita. Gemma 3 27B is available from DeepInfra, Novita.
DeepSeek-R1-0528
Gemma 3 27B
Outputs Comparison
Key Takeaways
DeepSeek-R1-0528
View detailsDeepSeek
Gemma 3 27B
View detailsDetailed Comparison
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FAQ
Common questions about DeepSeek-R1-0528 vs Gemma 3 27B.