Qwen 3 235B vs Llama 3.1 405B
Architecture Comparison
SpecQwen 3 235BLlama 3.1 405B
TypeMOEDENSE
Total Parameters235B405B
Active Parameters22B405B
Layers94126
Hidden Dimension5,12016,384
Attention Heads64128
KV Heads48
Context Length131,072131,072
Precision (default)BF16BF16
Total Experts128N/A
Active Experts8N/A
Memory Requirements
PrecisionQwen 3 235BLlama 3.1 405B
BF16 Weights470.0 GB810.0 GB
FP8 Weights235.0 GB405.0 GB
INT4 Weights117.5 GB202.5 GB
KV-Cache / Token192512 B516096 B
Activation Estimate3.00 GB5.00 GB
Minimum GPUs Needed (BF16)
H100 SXM7 GPUsN/A
L40SN/AN/A
Quality Benchmarks
BenchmarkQwen 3 235BLlama 3.1 405B
Overall8888
MMLU88.088.6
HumanEval62.061.0
GSM8K94.096.8
MT-Bench88.088.0
Qwen 3 235B
MMLU
88.0
HumanEval
62.0
GSM8K
94.0
MT-Bench
88.0
Llama 3.1 405B
MMLU
88.6
HumanEval
61.0
GSM8K
96.8
MT-Bench
88.0
Capabilities
FeatureQwen 3 235BLlama 3.1 405B
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 235B)
$3.00/M
Input: $1.50/M
Cheapest Output (Llama 3.1 405B)
$3.00/M
Input: $3.00/M
| Provider | Qwen 3 235B In $/M | Out $/M | Llama 3.1 405B In $/M | Out $/M |
|---|---|---|---|---|
| together | $1.50 | $3.00 | $3.50 | $3.50 |
| fireworks | $1.80 | $3.50 | $3.00 | $3.00 |
Recommendation Summary
- ‣Qwen 3 235B has a smaller memory footprint (470.0 GB vs 810.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Qwen 3 235B uses MOE architecture while Llama 3.1 405B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣Qwen 3 235B is stronger at code generation (HumanEval: 62.0 vs 61.0).
- ‣Llama 3.1 405B is better at math reasoning (GSM8K: 96.8 vs 94.0).