Qwen 2.5 7B vs Llama 3.1 405B
Architecture Comparison
SpecQwen 2.5 7BLlama 3.1 405B
TypeDENSEDENSE
Total Parameters7.6B405B
Active Parameters7.6B405B
Layers28126
Hidden Dimension3,58416,384
Attention Heads28128
KV Heads48
Context Length131,072131,072
Precision (default)BF16BF16
Memory Requirements
PrecisionQwen 2.5 7BLlama 3.1 405B
BF16 Weights15.2 GB810.0 GB
FP8 Weights7.6 GB405.0 GB
INT4 Weights3.8 GB202.5 GB
KV-Cache / Token57344 B516096 B
Activation Estimate1.00 GB5.00 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPUN/A
L40S1 GPUN/A
Quality Benchmarks
BenchmarkQwen 2.5 7BLlama 3.1 405B
Overall7088
MMLU74.288.6
HumanEval42.861.0
GSM8K82.096.8
MT-Bench79.088.0
Qwen 2.5 7B
MMLU
74.2
HumanEval
42.8
GSM8K
82.0
MT-Bench
79.0
Llama 3.1 405B
MMLU
88.6
HumanEval
61.0
GSM8K
96.8
MT-Bench
88.0
Capabilities
FeatureQwen 2.5 7BLlama 3.1 405B
Tool Use✓ Yes✓ Yes
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (Qwen 2.5 7B)
$0.20/M
Input: $0.20/M
Cheapest Output (Llama 3.1 405B)
$3.00/M
Input: $3.00/M
| Provider | Qwen 2.5 7B In $/M | Out $/M | Llama 3.1 405B In $/M | Out $/M |
|---|---|---|---|---|
| together | $0.20 | $0.20 | $3.50 | $3.50 |
| fireworks | $0.20 | $0.20 | $3.00 | $3.00 |
Recommendation Summary
- ‣Llama 3.1 405B scores higher on overall quality (88 vs 70).
- ‣Qwen 2.5 7B is cheaper per output token ($0.20/M vs $3.00/M).
- ‣Qwen 2.5 7B has a smaller memory footprint (15.2 GB vs 810.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Llama 3.1 405B is stronger at code generation (HumanEval: 61.0 vs 42.8).
- ‣Llama 3.1 405B is better at math reasoning (GSM8K: 96.8 vs 82.0).