Mixtral 8x22B vs Llama 3.1 70B
Architecture Comparison
SpecMixtral 8x22BLlama 3.1 70B
TypeMOEDENSE
Total Parameters141B70.6B
Active Parameters39B70.6B
Layers5680
Hidden Dimension6,1448,192
Attention Heads4864
KV Heads88
Context Length65,536131,072
Precision (default)BF16BF16
Total Experts8N/A
Active Experts2N/A
Memory Requirements
PrecisionMixtral 8x22BLlama 3.1 70B
BF16 Weights282.0 GB141.2 GB
FP8 Weights141.0 GB70.6 GB
INT4 Weights70.5 GB35.3 GB
KV-Cache / Token229376 B327680 B
Activation Estimate2.50 GB2.50 GB
Minimum GPUs Needed (BF16)
H100 SXM5 GPUs3 GPUs
L40S7 GPUs4 GPUs
Quality Benchmarks
BenchmarkMixtral 8x22BLlama 3.1 70B
Overall7382
MMLU77.883.6
HumanEval46.058.5
GSM8K78.493.0
MT-Bench80.085.0
Mixtral 8x22B
MMLU
77.8
HumanEval
46.0
GSM8K
78.4
MT-Bench
80.0
Llama 3.1 70B
MMLU
83.6
HumanEval
58.5
GSM8K
93.0
MT-Bench
85.0
Capabilities
FeatureMixtral 8x22BLlama 3.1 70B
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 70B)
$0.79/M
Input: $0.59/M
| Provider | Mixtral 8x22B In $/M | Out $/M | Llama 3.1 70B In $/M | Out $/M |
|---|---|---|---|---|
| groq | — | — | $0.59 | $0.79 |
| together | $1.20 | $1.20 | $0.88 | $0.88 |
| fireworks | — | — | $0.90 | $0.90 |
| mistral | $2.00 | $6.00 | — | — |
Recommendation Summary
- ‣Llama 3.1 70B scores higher on overall quality (82 vs 73).
- ‣Llama 3.1 70B is cheaper per output token ($0.79/M vs $1.20/M).
- ‣Llama 3.1 70B has a smaller memory footprint (141.2 GB vs 282.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.1 70B supports a longer context window (131,072 vs 65,536 tokens).
- ‣Mixtral 8x22B uses MOE architecture while Llama 3.1 70B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣Llama 3.1 70B is stronger at code generation (HumanEval: 58.5 vs 46.0).
- ‣Llama 3.1 70B is better at math reasoning (GSM8K: 93.0 vs 78.4).