Llama 3.1 8B vs Mistral 7B
Architecture Comparison
SpecLlama 3.1 8BMistral 7B
TypeDENSEDENSE
Total Parameters8.03B7.3B
Active Parameters8.03B7.3B
Layers3232
Hidden Dimension4,0964,096
Attention Heads3232
KV Heads88
Context Length131,07232,768
Precision (default)BF16BF16
Memory Requirements
PrecisionLlama 3.1 8BMistral 7B
BF16 Weights16.1 GB14.6 GB
FP8 Weights8.0 GB7.3 GB
INT4 Weights4.0 GB3.6 GB
KV-Cache / Token131072 B131072 B
Activation Estimate1.00 GB1.00 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU
Quality Benchmarks
BenchmarkLlama 3.1 8BMistral 7B
Overall6556
MMLU69.462.5
HumanEval40.232.0
GSM8K79.652.2
MT-Bench78.071.0
Llama 3.1 8B
MMLU
69.4
HumanEval
40.2
GSM8K
79.6
MT-Bench
78.0
Mistral 7B
MMLU
62.5
HumanEval
32.0
GSM8K
52.2
MT-Bench
71.0
Capabilities
FeatureLlama 3.1 8BMistral 7B
Tool Use✓ Yes✗ No
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✗ No
API Pricing Comparison
Cheapest Output (Llama 3.1 8B)
$0.08/M
Input: $0.05/M
Cheapest Output (Mistral 7B)
$0.07/M
Input: $0.07/M
| Provider | Llama 3.1 8B In $/M | Out $/M | Mistral 7B In $/M | Out $/M |
|---|---|---|---|---|
| deepinfra | — | — | $0.07 | $0.07 |
| groq | $0.05 | $0.08 | — | — |
| together | $0.18 | $0.18 | $0.20 | $0.20 |
| fireworks | $0.20 | $0.20 | — | — |
Recommendation Summary
- ‣Llama 3.1 8B scores higher on overall quality (65 vs 56).
- ‣Mistral 7B is cheaper per output token ($0.07/M vs $0.08/M).
- ‣Mistral 7B has a smaller memory footprint (14.6 GB vs 16.1 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.1 8B supports a longer context window (131,072 vs 32,768 tokens).
- ‣Llama 3.1 8B is stronger at code generation (HumanEval: 40.2 vs 32.0).
- ‣Llama 3.1 8B is better at math reasoning (GSM8K: 79.6 vs 52.2).