Llama 3.1 405B vs DeepSeek R1
Architecture Comparison
SpecLlama 3.1 405BDeepSeek R1
TypeDENSEMOE
Total Parameters405B671B
Active Parameters405B37B
Layers12661
Hidden Dimension16,3847,168
Attention Heads128128
KV Heads81
Context Length131,072131,072
Precision (default)BF16BF16
Total ExpertsN/A256
Active ExpertsN/A8
Memory Requirements
PrecisionLlama 3.1 405BDeepSeek R1
BF16 Weights810.0 GB1342.0 GB
FP8 Weights405.0 GB671.0 GB
INT4 Weights202.5 GB335.5 GB
KV-Cache / Token516096 B31232 B
Activation Estimate5.00 GB3.00 GB
Minimum GPUs Needed (BF16)
H100 SXMN/AN/A
L40SN/AN/A
Quality Benchmarks
BenchmarkLlama 3.1 405BDeepSeek R1
Overall8892
MMLU88.690.8
HumanEval61.071.7
GSM8K96.897.3
MT-Bench88.089.0
Llama 3.1 405B
MMLU
88.6
HumanEval
61.0
GSM8K
96.8
MT-Bench
88.0
DeepSeek R1
MMLU
90.8
HumanEval
71.7
GSM8K
97.3
MT-Bench
89.0
Capabilities
FeatureLlama 3.1 405BDeepSeek 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 405B)
$3.00/M
Input: $3.00/M
Cheapest Output (DeepSeek R1)
$2.19/M
Input: $0.55/M
| Provider | Llama 3.1 405B In $/M | Out $/M | DeepSeek R1 In $/M | Out $/M |
|---|---|---|---|---|
| deepseek | — | — | $0.55 | $2.19 |
| fireworks | $3.00 | $3.00 | — | — |
| together | $3.50 | $3.50 | $3.00 | $7.00 |
Recommendation Summary
- ‣DeepSeek R1 scores higher on overall quality (92 vs 88).
- ‣DeepSeek R1 is cheaper per output token ($2.19/M vs $3.00/M).
- ‣Llama 3.1 405B has a smaller memory footprint (810.0 GB vs 1342.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.1 405B 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 61.0).
- ‣DeepSeek R1 is better at math reasoning (GSM8K: 97.3 vs 96.8).