Mixtral 8x7B vs Llama 3.1 70B
Architecture Comparison
SpecMixtral 8x7BLlama 3.1 70B
TypeMOEDENSE
Total Parameters46.7B70.6B
Active Parameters12.9B70.6B
Layers3280
Hidden Dimension4,0968,192
Attention Heads3264
KV Heads88
Context Length32,768131,072
Precision (default)BF16BF16
Total Experts8N/A
Active Experts2N/A
Memory Requirements
PrecisionMixtral 8x7BLlama 3.1 70B
BF16 Weights93.4 GB141.2 GB
FP8 Weights46.7 GB70.6 GB
INT4 Weights23.4 GB35.3 GB
KV-Cache / Token131072 B327680 B
Activation Estimate1.50 GB2.50 GB
Minimum GPUs Needed (BF16)
H100 SXM2 GPUs3 GPUs
L40S3 GPUs4 GPUs
Quality Benchmarks
BenchmarkMixtral 8x7BLlama 3.1 70B
Overall6782
MMLU70.683.6
HumanEval40.258.5
GSM8K74.493.0
MT-Bench76.085.0
Mixtral 8x7B
MMLU
70.6
HumanEval
40.2
GSM8K
74.4
MT-Bench
76.0
Llama 3.1 70B
MMLU
83.6
HumanEval
58.5
GSM8K
93.0
MT-Bench
85.0
Capabilities
FeatureMixtral 8x7BLlama 3.1 70B
Tool Use✗ No✓ 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 8x7B)
$0.50/M
Input: $0.50/M
Cheapest Output (Llama 3.1 70B)
$0.79/M
Input: $0.59/M
| Provider | Mixtral 8x7B In $/M | Out $/M | Llama 3.1 70B In $/M | Out $/M |
|---|---|---|---|---|
| fireworks | $0.50 | $0.50 | $0.90 | $0.90 |
| together | $0.60 | $0.60 | $0.88 | $0.88 |
| groq | — | — | $0.59 | $0.79 |
Recommendation Summary
- ‣Llama 3.1 70B scores higher on overall quality (82 vs 67).
- ‣Mixtral 8x7B is cheaper per output token ($0.50/M vs $0.79/M).
- ‣Mixtral 8x7B has a smaller memory footprint (93.4 GB vs 141.2 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.1 70B supports a longer context window (131,072 vs 32,768 tokens).
- ‣Mixtral 8x7B 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 40.2).
- ‣Llama 3.1 70B is better at math reasoning (GSM8K: 93.0 vs 74.4).