Mixtral 8x22B vs Llama 3.1 8B
Architecture Comparison
SpecMixtral 8x22BLlama 3.1 8B
TypeMOEDENSE
Total Parameters141B8.03B
Active Parameters39B8.03B
Layers5632
Hidden Dimension6,1444,096
Attention Heads4832
KV Heads88
Context Length65,536131,072
Precision (default)BF16BF16
Total Experts8N/A
Active Experts2N/A
Memory Requirements
PrecisionMixtral 8x22BLlama 3.1 8B
BF16 Weights282.0 GB16.1 GB
FP8 Weights141.0 GB8.0 GB
INT4 Weights70.5 GB4.0 GB
KV-Cache / Token229376 B131072 B
Activation Estimate2.50 GB1.00 GB
Minimum GPUs Needed (BF16)
H100 SXM5 GPUs1 GPU
L40S7 GPUs1 GPU
Quality Benchmarks
BenchmarkMixtral 8x22BLlama 3.1 8B
Overall7365
MMLU77.869.4
HumanEval46.040.2
GSM8K78.479.6
MT-Bench80.078.0
Mixtral 8x22B
MMLU
77.8
HumanEval
46.0
GSM8K
78.4
MT-Bench
80.0
Llama 3.1 8B
MMLU
69.4
HumanEval
40.2
GSM8K
79.6
MT-Bench
78.0
Capabilities
FeatureMixtral 8x22BLlama 3.1 8B
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 (Mixtral 8x22B)
$1.20/M
Input: $1.20/M
Cheapest Output (Llama 3.1 8B)
$0.08/M
Input: $0.05/M
| Provider | Mixtral 8x22B In $/M | Out $/M | Llama 3.1 8B In $/M | Out $/M |
|---|---|---|---|---|
| groq | — | — | $0.05 | $0.08 |
| together | $1.20 | $1.20 | $0.18 | $0.18 |
| fireworks | — | — | $0.20 | $0.20 |
| mistral | $2.00 | $6.00 | — | — |
Recommendation Summary
- ‣Mixtral 8x22B scores higher on overall quality (73 vs 65).
- ‣Llama 3.1 8B is cheaper per output token ($0.08/M vs $1.20/M).
- ‣Llama 3.1 8B has a smaller memory footprint (16.1 GB vs 282.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.1 8B supports a longer context window (131,072 vs 65,536 tokens).
- ‣Mixtral 8x22B uses MOE architecture while Llama 3.1 8B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣Mixtral 8x22B is stronger at code generation (HumanEval: 46.0 vs 40.2).
- ‣Llama 3.1 8B is better at math reasoning (GSM8K: 79.6 vs 78.4).