Llama 4 Scout vs Gemma 3 12B
Architecture Comparison
SpecLlama 4 ScoutGemma 3 12B
TypeMOEDENSE
Total Parameters109B12B
Active Parameters17B12B
Layers4848
Hidden Dimension5,1203,072
Attention Heads4032
KV Heads816
Context Length10,485,760131,072
Precision (default)BF16BF16
Total Experts16N/A
Active Experts1N/A
Memory Requirements
PrecisionLlama 4 ScoutGemma 3 12B
BF16 Weights218.0 GB24.0 GB
FP8 Weights109.0 GB12.0 GB
INT4 Weights54.5 GB6.0 GB
KV-Cache / Token196608 B393216 B
Activation Estimate2.00 GB1.00 GB
Minimum GPUs Needed (BF16)
H100 SXM4 GPUs1 GPU
L40S6 GPUs1 GPU
Quality Benchmarks
BenchmarkLlama 4 ScoutGemma 3 12B
Overall7671
MMLU79.074.0
HumanEval55.044.0
GSM8K85.078.0
MT-Bench81.080.0
Llama 4 Scout
MMLU
79.0
HumanEval
55.0
GSM8K
85.0
MT-Bench
81.0
Gemma 3 12B
MMLU
74.0
HumanEval
44.0
GSM8K
78.0
MT-Bench
80.0
Capabilities
FeatureLlama 4 ScoutGemma 3 12B
Tool Use✓ Yes✓ Yes
Vision✓ Yes✓ Yes
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (Llama 4 Scout)
$0.30/M
Input: $0.18/M
Cheapest Output (Gemma 3 12B)
$0.10/M
Input: $0.05/M
| Provider | Llama 4 Scout In $/M | Out $/M | Gemma 3 12B In $/M | Out $/M |
|---|---|---|---|---|
| — | — | $0.05 | $0.10 | |
| together | $0.18 | $0.30 | $0.15 | $0.15 |
| fireworks | $0.20 | $0.35 | — | — |
Recommendation Summary
- ‣Llama 4 Scout scores higher on overall quality (76 vs 71).
- ‣Gemma 3 12B is cheaper per output token ($0.10/M vs $0.30/M).
- ‣Gemma 3 12B has a smaller memory footprint (24.0 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 Gemma 3 12B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣Llama 4 Scout is stronger at code generation (HumanEval: 55.0 vs 44.0).
- ‣Llama 4 Scout is better at math reasoning (GSM8K: 85.0 vs 78.0).