Qwen 3 32B vs Llama 3.3 70B
Architecture Comparison
SpecQwen 3 32BLlama 3.3 70B
TypeDENSEDENSE
Total Parameters32.8B70.6B
Active Parameters32.8B70.6B
Layers6480
Hidden Dimension5,1208,192
Attention Heads4064
KV Heads88
Context Length131,072131,072
Precision (default)BF16BF16
Memory Requirements
PrecisionQwen 3 32BLlama 3.3 70B
BF16 Weights65.6 GB141.2 GB
FP8 Weights32.8 GB70.6 GB
INT4 Weights16.4 GB35.3 GB
KV-Cache / Token262144 B327680 B
Activation Estimate2.00 GB2.50 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU3 GPUs
L40S2 GPUs4 GPUs
Quality Benchmarks
BenchmarkQwen 3 32BLlama 3.3 70B
Overall8084
MMLU82.086.0
HumanEval55.060.0
GSM8K90.094.0
MT-Bench84.086.0
Qwen 3 32B
MMLU
82.0
HumanEval
55.0
GSM8K
90.0
MT-Bench
84.0
Llama 3.3 70B
MMLU
86.0
HumanEval
60.0
GSM8K
94.0
MT-Bench
86.0
Capabilities
FeatureQwen 3 32BLlama 3.3 70B
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 32B)
$0.80/M
Input: $0.80/M
Cheapest Output (Llama 3.3 70B)
$0.79/M
Input: $0.59/M
| Provider | Qwen 3 32B In $/M | Out $/M | Llama 3.3 70B In $/M | Out $/M |
|---|---|---|---|---|
| groq | — | — | $0.59 | $0.79 |
| together | $0.80 | $0.80 | $0.88 | $0.88 |
| fireworks | $0.90 | $0.90 | $0.90 | $0.90 |
Recommendation Summary
- ‣Llama 3.3 70B scores higher on overall quality (84 vs 80).
- ‣Llama 3.3 70B is cheaper per output token ($0.79/M vs $0.80/M).
- ‣Qwen 3 32B has a smaller memory footprint (65.6 GB vs 141.2 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.3 70B is stronger at code generation (HumanEval: 60.0 vs 55.0).
- ‣Llama 3.3 70B is better at math reasoning (GSM8K: 94.0 vs 90.0).