DeepSeek-R1 vs Gemma 2 27B
Comparing DeepSeek-R1 and Gemma 2 27B across benchmarks, pricing, and capabilities.
DeepSeek · Google · Updated for 2026
Which is better?
DeepSeek-R1 and Gemma 2 27B trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-R1
- you want the most recent training data — it shipped Jan 2025
Choose Gemma 2 27B
- you are already invested in the Google ecosystem
At a glance
The differences that matter most.
Individual benchmarks
0 reported for DeepSeek-R1 · 16 for Gemma 2 27B
DeepSeek-R1 and Gemma 2 27Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Model Size
Parameter count comparison
DeepSeek-R1 has 643.8B more parameters than Gemma 2 27B, making it 2366.9% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-R1 specifies input context (131,072 tokens). Only DeepSeek-R1 specifies output context (131,072 tokens).
License
Usage and distribution terms
DeepSeek-R1 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.
MIT
Open weights
Gemma
Open weights
Release Timeline
When each model was launched
DeepSeek-R1 was released on 2025-01-20, while Gemma 2 27B was released on 2024-06-27.
DeepSeek-R1 is 7 months newer than Gemma 2 27B.
Jan 20, 2025
1.6 years ago
6mo newerJun 27, 2024
2.2 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1 and Gemma 2 27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1 vs Gemma 2 27B.