DeepSeek R1 vs Gemma 3 27B
Architecture Comparison
SpecDeepSeek R1Gemma 3 27B
TypeMOEDENSE
Total Parameters671B27B
Active Parameters37B27B
Layers6162
Hidden Dimension7,1683,584
Attention Heads12832
KV Heads116
Context Length131,072131,072
Precision (default)BF16BF16
Total Experts256N/A
Active Experts8N/A
Memory Requirements
PrecisionDeepSeek R1Gemma 3 27B
BF16 Weights1342.0 GB54.0 GB
FP8 Weights671.0 GB27.0 GB
INT4 Weights335.5 GB13.5 GB
KV-Cache / Token31232 B507904 B
Activation Estimate3.00 GB1.50 GB
Minimum GPUs Needed (BF16)
H100 SXMN/A1 GPU
L40SN/A2 GPUs
Quality Benchmarks
BenchmarkDeepSeek R1Gemma 3 27B
Overall9276
MMLU90.878.0
HumanEval71.748.0
GSM8K97.385.0
MT-Bench89.082.0
DeepSeek R1
MMLU
90.8
HumanEval
71.7
GSM8K
97.3
MT-Bench
89.0
Gemma 3 27B
MMLU
78.0
HumanEval
48.0
GSM8K
85.0
MT-Bench
82.0
Capabilities
FeatureDeepSeek R1Gemma 3 27B
Tool Use✓ Yes✓ Yes
Vision✗ No✓ Yes
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✓ Yes✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (DeepSeek R1)
$2.19/M
Input: $0.55/M
Cheapest Output (Gemma 3 27B)
$0.20/M
Input: $0.10/M
| Provider | DeepSeek R1 In $/M | Out $/M | Gemma 3 27B In $/M | Out $/M |
|---|---|---|---|---|
| — | — | $0.10 | $0.20 | |
| together | $3.00 | $7.00 | $0.30 | $0.30 |
| deepseek | $0.55 | $2.19 | — | — |
Recommendation Summary
- ‣DeepSeek R1 scores higher on overall quality (92 vs 76).
- ‣Gemma 3 27B is cheaper per output token ($0.20/M vs $2.19/M).
- ‣Gemma 3 27B has a smaller memory footprint (54.0 GB vs 1342.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣DeepSeek R1 uses MOE architecture while Gemma 3 27B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣DeepSeek R1 is stronger at code generation (HumanEval: 71.7 vs 48.0).
- ‣DeepSeek R1 is better at math reasoning (GSM8K: 97.3 vs 85.0).