DeepSeek V3 vs Llama 3.1 70B
Architecture Comparison
SpecDeepSeek V3Llama 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 V3Llama 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 V3Llama 3.1 70B
Overall8682
MMLU87.183.6
HumanEval65.058.5
GSM8K89.393.0
MT-Bench87.085.0
DeepSeek V3
MMLU
87.1
HumanEval
65.0
GSM8K
89.3
MT-Bench
87.0
Llama 3.1 70B
MMLU
83.6
HumanEval
58.5
GSM8K
93.0
MT-Bench
85.0
Capabilities
FeatureDeepSeek V3Llama 3.1 70B
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 70B)
$0.79/M
Input: $0.59/M
| Provider | DeepSeek V3 In $/M | Out $/M | Llama 3.1 70B In $/M | Out $/M |
|---|---|---|---|---|
| deepseek | $0.28 | $0.42 | — | — |
| groq | — | — | $0.59 | $0.79 |
| together | $0.50 | $2.80 | $0.88 | $0.88 |
| fireworks | — | — | $0.90 | $0.90 |
Recommendation Summary
- ‣DeepSeek V3 scores higher on overall quality (86 vs 82).
- ‣DeepSeek V3 is cheaper per output token ($0.42/M vs $0.79/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 V3 uses MOE architecture while Llama 3.1 70B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣DeepSeek V3 is stronger at code generation (HumanEval: 65.0 vs 58.5).
- ‣Llama 3.1 70B is better at math reasoning (GSM8K: 93.0 vs 89.3).