Qwen 3 4B vs Llama 3.2 3B
Architecture Comparison
SpecQwen 3 4BLlama 3.2 3B
TypeDENSEDENSE
Total Parameters4B3.21B
Active Parameters4B3.21B
Layers3628
Hidden Dimension2,5603,072
Attention Heads3224
KV Heads88
Context Length131,072131,072
Precision (default)BF16BF16
Memory Requirements
PrecisionQwen 3 4BLlama 3.2 3B
BF16 Weights8.0 GB6.4 GB
FP8 Weights4.0 GB3.2 GB
INT4 Weights2.0 GB1.6 GB
KV-Cache / Token147456 B114688 B
Activation Estimate0.50 GB0.50 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU
Quality Benchmarks
BenchmarkQwen 3 4BLlama 3.2 3B
Overall5755
MMLU64.063.4
HumanEval35.033.0
GSM8K65.068.0
MT-Bench73.073.0
Qwen 3 4B
MMLU
64.0
HumanEval
35.0
GSM8K
65.0
MT-Bench
73.0
Llama 3.2 3B
MMLU
63.4
HumanEval
33.0
GSM8K
68.0
MT-Bench
73.0
Capabilities
FeatureQwen 3 4BLlama 3.2 3B
Tool Use✓ Yes✓ Yes
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✓ Yes✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (Qwen 3 4B)
$0.10/M
Input: $0.10/M
Cheapest Output (Llama 3.2 3B)
$0.06/M
Input: $0.06/M
| Provider | Qwen 3 4B In $/M | Out $/M | Llama 3.2 3B In $/M | Out $/M |
|---|---|---|---|---|
| together | $0.10 | $0.10 | $0.06 | $0.06 |
| fireworks | — | — | $0.10 | $0.10 |
Recommendation Summary
- ‣Qwen 3 4B scores higher on overall quality (57 vs 55).
- ‣Llama 3.2 3B is cheaper per output token ($0.06/M vs $0.10/M).
- ‣Llama 3.2 3B has a smaller memory footprint (6.4 GB vs 8.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Qwen 3 4B is stronger at code generation (HumanEval: 35.0 vs 33.0).
- ‣Llama 3.2 3B is better at math reasoning (GSM8K: 68.0 vs 65.0).