Llama 4 Scout vs Qwen 3 8B
Architecture Comparison
SpecLlama 4 ScoutQwen 3 8B
TypeMOEDENSE
Total Parameters109B8.2B
Active Parameters17B8.2B
Layers4836
Hidden Dimension5,1204,096
Attention Heads4032
KV Heads88
Context Length10,485,760131,072
Precision (default)BF16BF16
Total Experts16N/A
Active Experts1N/A
Memory Requirements
PrecisionLlama 4 ScoutQwen 3 8B
BF16 Weights218.0 GB16.4 GB
FP8 Weights109.0 GB8.2 GB
INT4 Weights54.5 GB4.1 GB
KV-Cache / Token196608 B147456 B
Activation Estimate2.00 GB1.00 GB
Minimum GPUs Needed (BF16)
H100 SXM4 GPUs1 GPU
L40S6 GPUs1 GPU
Quality Benchmarks
BenchmarkLlama 4 ScoutQwen 3 8B
Overall7667
MMLU79.072.0
HumanEval55.042.0
GSM8K85.078.0
MT-Bench81.077.0
Llama 4 Scout
MMLU
79.0
HumanEval
55.0
GSM8K
85.0
MT-Bench
81.0
Qwen 3 8B
MMLU
72.0
HumanEval
42.0
GSM8K
78.0
MT-Bench
77.0
Capabilities
FeatureLlama 4 ScoutQwen 3 8B
Tool Use✓ Yes✓ Yes
Vision✓ Yes✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✓ Yes
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (Llama 4 Scout)
$0.30/M
Input: $0.18/M
Cheapest Output (Qwen 3 8B)
$0.20/M
Input: $0.20/M
| Provider | Llama 4 Scout In $/M | Out $/M | Qwen 3 8B In $/M | Out $/M |
|---|---|---|---|---|
| together | $0.18 | $0.30 | $0.20 | $0.20 |
| fireworks | $0.20 | $0.35 | $0.20 | $0.20 |
Recommendation Summary
- ‣Llama 4 Scout scores higher on overall quality (76 vs 67).
- ‣Qwen 3 8B is cheaper per output token ($0.20/M vs $0.30/M).
- ‣Qwen 3 8B has a smaller memory footprint (16.4 GB vs 218.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 4 Scout supports a longer context window (10,485,760 vs 131,072 tokens).
- ‣Llama 4 Scout uses MOE architecture while Qwen 3 8B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣Llama 4 Scout is stronger at code generation (HumanEval: 55.0 vs 42.0).
- ‣Llama 4 Scout is better at math reasoning (GSM8K: 85.0 vs 78.0).