Llama 3.1 405B vs Qwen 3 235B
Architecture Comparison
SpecLlama 3.1 405BQwen 3 235B
TypeDENSEMOE
Total Parameters405B235B
Active Parameters405B22B
Layers12694
Hidden Dimension16,3845,120
Attention Heads12864
KV Heads84
Context Length131,072131,072
Precision (default)BF16BF16
Total ExpertsN/A128
Active ExpertsN/A8
Memory Requirements
PrecisionLlama 3.1 405BQwen 3 235B
BF16 Weights810.0 GB470.0 GB
FP8 Weights405.0 GB235.0 GB
INT4 Weights202.5 GB117.5 GB
KV-Cache / Token516096 B192512 B
Activation Estimate5.00 GB3.00 GB
Minimum GPUs Needed (BF16)
H100 SXMN/A7 GPUs
L40SN/AN/A
Quality Benchmarks
BenchmarkLlama 3.1 405BQwen 3 235B
Overall8888
MMLU88.688.0
HumanEval61.062.0
GSM8K96.894.0
MT-Bench88.088.0
Llama 3.1 405B
MMLU
88.6
HumanEval
61.0
GSM8K
96.8
MT-Bench
88.0
Qwen 3 235B
MMLU
88.0
HumanEval
62.0
GSM8K
94.0
MT-Bench
88.0
Capabilities
FeatureLlama 3.1 405BQwen 3 235B
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 (Llama 3.1 405B)
$3.00/M
Input: $3.00/M
Cheapest Output (Qwen 3 235B)
$3.00/M
Input: $1.50/M
| Provider | Llama 3.1 405B In $/M | Out $/M | Qwen 3 235B In $/M | Out $/M |
|---|---|---|---|---|
| together | $3.50 | $3.50 | $1.50 | $3.00 |
| fireworks | $3.00 | $3.00 | $1.80 | $3.50 |
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.
- ‣Llama 3.1 405B uses DENSE architecture while Qwen 3 235B uses MOE. 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).