Llama 3.1 8B vs DeepSeek R1
Architecture Comparison
SpecLlama 3.1 8BDeepSeek R1
TypeDENSEMOE
Total Parameters8.03B671B
Active Parameters8.03B37B
Layers3261
Hidden Dimension4,0967,168
Attention Heads32128
KV Heads81
Context Length131,072131,072
Precision (default)BF16BF16
Total ExpertsN/A256
Active ExpertsN/A8
Memory Requirements
PrecisionLlama 3.1 8BDeepSeek R1
BF16 Weights16.1 GB1342.0 GB
FP8 Weights8.0 GB671.0 GB
INT4 Weights4.0 GB335.5 GB
KV-Cache / Token131072 B31232 B
Activation Estimate1.00 GB3.00 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPUN/A
L40S1 GPUN/A
Quality Benchmarks
BenchmarkLlama 3.1 8BDeepSeek R1
Overall6592
MMLU69.490.8
HumanEval40.271.7
GSM8K79.697.3
MT-Bench78.089.0
Llama 3.1 8B
MMLU
69.4
HumanEval
40.2
GSM8K
79.6
MT-Bench
78.0
DeepSeek R1
MMLU
90.8
HumanEval
71.7
GSM8K
97.3
MT-Bench
89.0
Capabilities
FeatureLlama 3.1 8BDeepSeek 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 8B)
$0.08/M
Input: $0.05/M
Cheapest Output (DeepSeek R1)
$2.19/M
Input: $0.55/M
| Provider | Llama 3.1 8B In $/M | Out $/M | DeepSeek R1 In $/M | Out $/M |
|---|---|---|---|---|
| groq | $0.05 | $0.08 | — | — |
| together | $0.18 | $0.18 | $3.00 | $7.00 |
| 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.
- ‣Llama 3.1 8B 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 40.2).
- ‣DeepSeek R1 is better at math reasoning (GSM8K: 97.3 vs 79.6).