Mixtral 8x22B vs Llama 3.1 405B
Architecture Comparison
SpecMixtral 8x22BLlama 3.1 405B
TypeMOEDENSE
Total Parameters141B405B
Active Parameters39B405B
Layers56126
Hidden Dimension6,14416,384
Attention Heads48128
KV Heads88
Context Length65,536131,072
Precision (default)BF16BF16
Total Experts8N/A
Active Experts2N/A
Memory Requirements
PrecisionMixtral 8x22BLlama 3.1 405B
BF16 Weights282.0 GB810.0 GB
FP8 Weights141.0 GB405.0 GB
INT4 Weights70.5 GB202.5 GB
KV-Cache / Token229376 B516096 B
Activation Estimate2.50 GB5.00 GB
Minimum GPUs Needed (BF16)
H100 SXM5 GPUsN/A
L40S7 GPUsN/A
Quality Benchmarks
BenchmarkMixtral 8x22BLlama 3.1 405B
Overall7388
MMLU77.888.6
HumanEval46.061.0
GSM8K78.496.8
MT-Bench80.088.0
Mixtral 8x22B
MMLU
77.8
HumanEval
46.0
GSM8K
78.4
MT-Bench
80.0
Llama 3.1 405B
MMLU
88.6
HumanEval
61.0
GSM8K
96.8
MT-Bench
88.0
Capabilities
FeatureMixtral 8x22BLlama 3.1 405B
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 405B)
$3.00/M
Input: $3.00/M
| Provider | Mixtral 8x22B In $/M | Out $/M | Llama 3.1 405B In $/M | Out $/M |
|---|---|---|---|---|
| together | $1.20 | $1.20 | $3.50 | $3.50 |
| fireworks | — | — | $3.00 | $3.00 |
| mistral | $2.00 | $6.00 | — | — |
Recommendation Summary
- ‣Llama 3.1 405B scores higher on overall quality (88 vs 73).
- ‣Mixtral 8x22B is cheaper per output token ($1.20/M vs $3.00/M).
- ‣Mixtral 8x22B has a smaller memory footprint (282.0 GB vs 810.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.1 405B supports a longer context window (131,072 vs 65,536 tokens).
- ‣Mixtral 8x22B uses MOE architecture while Llama 3.1 405B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣Llama 3.1 405B is stronger at code generation (HumanEval: 61.0 vs 46.0).
- ‣Llama 3.1 405B is better at math reasoning (GSM8K: 96.8 vs 78.4).