Llama 3.1 70B vs DeepSeek R1
Architecture Comparison
SpecLlama 3.1 70BDeepSeek R1
TypeDENSEMOE
Total Parameters70.6B671B
Active Parameters70.6B37B
Layers8061
Hidden Dimension8,1927,168
Attention Heads64128
KV Heads81
Context Length131,072131,072
Precision (default)BF16BF16
Total ExpertsN/A256
Active ExpertsN/A8
Memory Requirements
PrecisionLlama 3.1 70BDeepSeek R1
BF16 Weights141.2 GB1342.0 GB
FP8 Weights70.6 GB671.0 GB
INT4 Weights35.3 GB335.5 GB
KV-Cache / Token327680 B31232 B
Activation Estimate2.50 GB3.00 GB
Minimum GPUs Needed (BF16)
H100 SXM3 GPUsN/A
L40S4 GPUsN/A
Quality Benchmarks
BenchmarkLlama 3.1 70BDeepSeek R1
Overall8292
MMLU83.690.8
HumanEval58.571.7
GSM8K93.097.3
MT-Bench85.089.0
Llama 3.1 70B
MMLU
83.6
HumanEval
58.5
GSM8K
93.0
MT-Bench
85.0
DeepSeek R1
MMLU
90.8
HumanEval
71.7
GSM8K
97.3
MT-Bench
89.0
Capabilities
FeatureLlama 3.1 70BDeepSeek R1
Tool Use✓ Yes✓ Yes
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✓ Yes
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (Llama 3.1 70B)
$0.79/M
Input: $0.59/M
Cheapest Output (DeepSeek R1)
$2.19/M
Input: $0.55/M
| Provider | Llama 3.1 70B In $/M | Out $/M | DeepSeek R1 In $/M | Out $/M |
|---|---|---|---|---|
| groq | $0.59 | $0.79 | — | — |
| together | $0.88 | $0.88 | $3.00 | $7.00 |
| 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.
- ‣Llama 3.1 70B uses DENSE architecture while DeepSeek R1 uses MOE. 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).