DeepSeek V3 vs Llama 3.1 8B
Architecture Comparison
SpecDeepSeek V3Llama 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 V3Llama 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 V3Llama 3.1 8B
Overall8665
MMLU87.169.4
HumanEval65.040.2
GSM8K89.379.6
MT-Bench87.078.0
DeepSeek V3
MMLU
87.1
HumanEval
65.0
GSM8K
89.3
MT-Bench
87.0
Llama 3.1 8B
MMLU
69.4
HumanEval
40.2
GSM8K
79.6
MT-Bench
78.0
Capabilities
FeatureDeepSeek V3Llama 3.1 8B
Tool Use✓ Yes✓ Yes
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (DeepSeek V3)
$0.42/M
Input: $0.28/M
Cheapest Output (Llama 3.1 8B)
$0.08/M
Input: $0.05/M
| Provider | DeepSeek V3 In $/M | Out $/M | Llama 3.1 8B In $/M | Out $/M |
|---|---|---|---|---|
| groq | — | — | $0.05 | $0.08 |
| together | $0.50 | $2.80 | $0.18 | $0.18 |
| fireworks | — | — | $0.20 | $0.20 |
| deepseek | $0.28 | $0.42 | — | — |
Recommendation Summary
- ‣DeepSeek V3 scores higher on overall quality (86 vs 65).
- ‣Llama 3.1 8B is cheaper per output token ($0.08/M vs $0.42/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 V3 uses MOE architecture while Llama 3.1 8B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣DeepSeek V3 is stronger at code generation (HumanEval: 65.0 vs 40.2).
- ‣DeepSeek V3 is better at math reasoning (GSM8K: 89.3 vs 79.6).