Llama 3.3 70B vs DeepSeek V3
Architecture Comparison
SpecLlama 3.3 70BDeepSeek V3
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 3.3 70BDeepSeek V3
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 3.3 70BDeepSeek V3
Overall8486
MMLU86.087.1
HumanEval60.065.0
GSM8K94.089.3
MT-Bench86.087.0
Llama 3.3 70B
MMLU
86.0
HumanEval
60.0
GSM8K
94.0
MT-Bench
86.0
DeepSeek V3
MMLU
87.1
HumanEval
65.0
GSM8K
89.3
MT-Bench
87.0
Capabilities
FeatureLlama 3.3 70BDeepSeek V3
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 (Llama 3.3 70B)
$0.79/M
Input: $0.59/M
Cheapest Output (DeepSeek V3)
$0.42/M
Input: $0.28/M
| Provider | Llama 3.3 70B In $/M | Out $/M | DeepSeek V3 In $/M | Out $/M |
|---|---|---|---|---|
| deepseek | — | — | $0.28 | $0.42 |
| groq | $0.59 | $0.79 | — | — |
| together | $0.88 | $0.88 | $0.50 | $2.80 |
| fireworks | $0.90 | $0.90 | — | — |
Recommendation Summary
- ‣DeepSeek V3 scores higher on overall quality (86 vs 84).
- ‣DeepSeek V3 is cheaper per output token ($0.42/M vs $0.79/M).
- ‣Llama 3.3 70B has a smaller memory footprint (141.2 GB vs 1342.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.3 70B uses DENSE architecture while DeepSeek V3 uses MOE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣DeepSeek V3 is stronger at code generation (HumanEval: 65.0 vs 60.0).
- ‣Llama 3.3 70B is better at math reasoning (GSM8K: 94.0 vs 89.3).