DeepSeek R1 vs Llama 3.1 70B
Architecture Comparison
SpecDeepSeek R1Llama 3.1 70B
TypeMOEDENSE
Total Parameters671B70.6B
Active Parameters37B70.6B
Layers6180
Hidden Dimension7,1688,192
Attention Heads12864
KV Heads18
Context Length131,072131,072
Precision (default)BF16BF16
Total Experts256N/A
Active Experts8N/A
Memory Requirements
PrecisionDeepSeek R1Llama 3.1 70B
BF16 Weights1342.0 GB141.2 GB
FP8 Weights671.0 GB70.6 GB
INT4 Weights335.5 GB35.3 GB
KV-Cache / Token31232 B327680 B
Activation Estimate3.00 GB2.50 GB
Minimum GPUs Needed (BF16)
H100 SXMN/A3 GPUs
L40SN/A4 GPUs
Quality Benchmarks
BenchmarkDeepSeek R1Llama 3.1 70B
Overall9282
MMLU90.883.6
HumanEval71.758.5
GSM8K97.393.0
MT-Bench89.085.0
DeepSeek R1
MMLU
90.8
HumanEval
71.7
GSM8K
97.3
MT-Bench
89.0
Llama 3.1 70B
MMLU
83.6
HumanEval
58.5
GSM8K
93.0
MT-Bench
85.0
Capabilities
FeatureDeepSeek R1Llama 3.1 70B
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 70B)
$0.79/M
Input: $0.59/M
| Provider | DeepSeek R1 In $/M | Out $/M | Llama 3.1 70B In $/M | Out $/M |
|---|---|---|---|---|
| groq | — | — | $0.59 | $0.79 |
| together | $3.00 | $7.00 | $0.88 | $0.88 |
| fireworks | — | — | $0.90 | $0.90 |
| deepseek | $0.55 | $2.19 | — | — |
Recommendation Summary
- ‣DeepSeek R1 scores higher on overall quality (92 vs 82).
- ‣Llama 3.1 70B is cheaper per output token ($0.79/M vs $2.19/M).
- ‣Llama 3.1 70B has a smaller memory footprint (141.2 GB vs 1342.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣DeepSeek R1 uses MOE architecture while Llama 3.1 70B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣DeepSeek R1 is stronger at code generation (HumanEval: 71.7 vs 58.5).
- ‣DeepSeek R1 is better at math reasoning (GSM8K: 97.3 vs 93.0).