DeepSeek R1 vs Llama 3.1 405B
Architecture Comparison
SpecDeepSeek R1Llama 3.1 405B
TypeMOEDENSE
Total Parameters671B405B
Active Parameters37B405B
Layers61126
Hidden Dimension7,16816,384
Attention Heads128128
KV Heads18
Context Length131,072131,072
Precision (default)BF16BF16
Total Experts256N/A
Active Experts8N/A
Memory Requirements
PrecisionDeepSeek R1Llama 3.1 405B
BF16 Weights1342.0 GB810.0 GB
FP8 Weights671.0 GB405.0 GB
INT4 Weights335.5 GB202.5 GB
KV-Cache / Token31232 B516096 B
Activation Estimate3.00 GB5.00 GB
Minimum GPUs Needed (BF16)
H100 SXMN/AN/A
L40SN/AN/A
Quality Benchmarks
BenchmarkDeepSeek R1Llama 3.1 405B
Overall9288
MMLU90.888.6
HumanEval71.761.0
GSM8K97.396.8
MT-Bench89.088.0
DeepSeek R1
MMLU
90.8
HumanEval
71.7
GSM8K
97.3
MT-Bench
89.0
Llama 3.1 405B
MMLU
88.6
HumanEval
61.0
GSM8K
96.8
MT-Bench
88.0
Capabilities
FeatureDeepSeek R1Llama 3.1 405B
Tool Use✓ Yes✓ Yes
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✓ Yes✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (DeepSeek R1)
$2.19/M
Input: $0.55/M
Cheapest Output (Llama 3.1 405B)
$3.00/M
Input: $3.00/M
| Provider | DeepSeek R1 In $/M | Out $/M | Llama 3.1 405B In $/M | Out $/M |
|---|---|---|---|---|
| deepseek | $0.55 | $2.19 | — | — |
| fireworks | — | — | $3.00 | $3.00 |
| together | $3.00 | $7.00 | $3.50 | $3.50 |
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.
- ‣DeepSeek R1 uses MOE architecture while Llama 3.1 405B uses DENSE. 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).