Phi-4 vs Llama 3.1 8B
Architecture Comparison
SpecPhi-4Llama 3.1 8B
TypeDENSEDENSE
Total Parameters14.7B8.03B
Active Parameters14.7B8.03B
Layers4032
Hidden Dimension5,1204,096
Attention Heads4032
KV Heads108
Context Length16,384131,072
Precision (default)BF16BF16
Memory Requirements
PrecisionPhi-4Llama 3.1 8B
BF16 Weights29.4 GB16.1 GB
FP8 Weights14.7 GB8.0 GB
INT4 Weights7.3 GB4.0 GB
KV-Cache / Token204800 B131072 B
Activation Estimate1.50 GB1.00 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU
Quality Benchmarks
BenchmarkPhi-4Llama 3.1 8B
Overall8365
MMLU84.869.4
HumanEval67.040.2
GSM8K93.079.6
MT-Bench85.078.0
Phi-4
MMLU
84.8
HumanEval
67.0
GSM8K
93.0
MT-Bench
85.0
Llama 3.1 8B
MMLU
69.4
HumanEval
40.2
GSM8K
79.6
MT-Bench
78.0
Capabilities
FeaturePhi-4Llama 3.1 8B
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 (Phi-4)
$0.14/M
Input: $0.07/M
Cheapest Output (Llama 3.1 8B)
$0.08/M
Input: $0.05/M
| Provider | Phi-4 In $/M | Out $/M | Llama 3.1 8B In $/M | Out $/M |
|---|---|---|---|---|
| groq | — | — | $0.05 | $0.08 |
| azure | $0.07 | $0.14 | — | — |
| together | $0.20 | $0.20 | $0.18 | $0.18 |
| fireworks | — | — | $0.20 | $0.20 |
Recommendation Summary
- ‣Phi-4 scores higher on overall quality (83 vs 65).
- ‣Llama 3.1 8B is cheaper per output token ($0.08/M vs $0.14/M).
- ‣Llama 3.1 8B has a smaller memory footprint (16.1 GB vs 29.4 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.1 8B supports a longer context window (131,072 vs 16,384 tokens).
- ‣Phi-4 is stronger at code generation (HumanEval: 67.0 vs 40.2).
- ‣Phi-4 is better at math reasoning (GSM8K: 93.0 vs 79.6).