DeepSeek R1 vs Llama 3.1 8B
Architecture Comparison
SpecDeepSeek R1Llama 3.1 8B
TypeMOEDENSE
Total Parameters671B8.03B
Active Parameters37B8.03B
Layers6132
Hidden Dimension7,1684,096
Attention Heads12832
KV Heads18
Context Length131,072131,072
Precision (default)BF16BF16
Total Experts256N/A
Active Experts8N/A
Memory Requirements
PrecisionDeepSeek R1Llama 3.1 8B
BF16 Weights1342.0 GB16.1 GB
FP8 Weights671.0 GB8.0 GB
INT4 Weights335.5 GB4.0 GB
KV-Cache / Token31232 B131072 B
Activation Estimate3.00 GB1.00 GB
Minimum GPUs Needed (BF16)
H100 SXMN/A1 GPU
L40SN/A1 GPU
Quality Benchmarks
BenchmarkDeepSeek R1Llama 3.1 8B
Overall9265
MMLU90.869.4
HumanEval71.740.2
GSM8K97.379.6
MT-Bench89.078.0
DeepSeek R1
MMLU
90.8
HumanEval
71.7
GSM8K
97.3
MT-Bench
89.0
Llama 3.1 8B
MMLU
69.4
HumanEval
40.2
GSM8K
79.6
MT-Bench
78.0
Capabilities
FeatureDeepSeek R1Llama 3.1 8B
Tool Use✓ Yes✓ Yes
Vision✗ No✗ No
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 (Llama 3.1 8B)
$0.08/M
Input: $0.05/M
| Provider | DeepSeek R1 In $/M | Out $/M | Llama 3.1 8B In $/M | Out $/M |
|---|---|---|---|---|
| groq | — | — | $0.05 | $0.08 |
| together | $3.00 | $7.00 | $0.18 | $0.18 |
| fireworks | — | — | $0.20 | $0.20 |
| deepseek | $0.55 | $2.19 | — | — |
Recommendation Summary
- ‣DeepSeek R1 scores higher on overall quality (92 vs 65).
- ‣Llama 3.1 8B is cheaper per output token ($0.08/M vs $2.19/M).
- ‣Llama 3.1 8B has a smaller memory footprint (16.1 GB vs 1342.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣DeepSeek R1 uses MOE architecture while Llama 3.1 8B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣DeepSeek R1 is stronger at code generation (HumanEval: 71.7 vs 40.2).
- ‣DeepSeek R1 is better at math reasoning (GSM8K: 97.3 vs 79.6).