DeepSeek V3 vs Qwen 3 32B
Architecture Comparison
SpecDeepSeek V3Qwen 3 32B
TypeMOEDENSE
Total Parameters671B32.8B
Active Parameters37B32.8B
Layers6164
Hidden Dimension7,1685,120
Attention Heads12840
KV Heads18
Context Length131,072131,072
Precision (default)BF16BF16
Total Experts256N/A
Active Experts8N/A
Memory Requirements
PrecisionDeepSeek V3Qwen 3 32B
BF16 Weights1342.0 GB65.6 GB
FP8 Weights671.0 GB32.8 GB
INT4 Weights335.5 GB16.4 GB
KV-Cache / Token31232 B262144 B
Activation Estimate3.00 GB2.00 GB
Minimum GPUs Needed (BF16)
H100 SXMN/A1 GPU
L40SN/A2 GPUs
Quality Benchmarks
BenchmarkDeepSeek V3Qwen 3 32B
Overall8680
MMLU87.182.0
HumanEval65.055.0
GSM8K89.390.0
MT-Bench87.084.0
DeepSeek V3
MMLU
87.1
HumanEval
65.0
GSM8K
89.3
MT-Bench
87.0
Qwen 3 32B
MMLU
82.0
HumanEval
55.0
GSM8K
90.0
MT-Bench
84.0
Capabilities
FeatureDeepSeek V3Qwen 3 32B
Tool Use✓ Yes✓ Yes
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✓ Yes
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (DeepSeek V3)
$0.42/M
Input: $0.28/M
Cheapest Output (Qwen 3 32B)
$0.80/M
Input: $0.80/M
| Provider | DeepSeek V3 In $/M | Out $/M | Qwen 3 32B In $/M | Out $/M |
|---|---|---|---|---|
| deepseek | $0.28 | $0.42 | — | — |
| together | $0.50 | $2.80 | $0.80 | $0.80 |
| fireworks | — | — | $0.90 | $0.90 |
Recommendation Summary
- ‣DeepSeek V3 scores higher on overall quality (86 vs 80).
- ‣DeepSeek V3 is cheaper per output token ($0.42/M vs $0.80/M).
- ‣Qwen 3 32B has a smaller memory footprint (65.6 GB vs 1342.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣DeepSeek V3 uses MOE architecture while Qwen 3 32B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣DeepSeek V3 is stronger at code generation (HumanEval: 65.0 vs 55.0).
- ‣Qwen 3 32B is better at math reasoning (GSM8K: 90.0 vs 89.3).