Qwen 2.5 7B vs Llama 3.1 8B
Architecture Comparison
SpecQwen 2.5 7BLlama 3.1 8B
TypeDENSEDENSE
Total Parameters7.6B8.03B
Active Parameters7.6B8.03B
Layers2832
Hidden Dimension3,5844,096
Attention Heads2832
KV Heads48
Context Length131,072131,072
Precision (default)BF16BF16
Memory Requirements
PrecisionQwen 2.5 7BLlama 3.1 8B
BF16 Weights15.2 GB16.1 GB
FP8 Weights7.6 GB8.0 GB
INT4 Weights3.8 GB4.0 GB
KV-Cache / Token57344 B131072 B
Activation Estimate1.00 GB1.00 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU
Quality Benchmarks
BenchmarkQwen 2.5 7BLlama 3.1 8B
Overall7065
MMLU74.269.4
HumanEval42.840.2
GSM8K82.079.6
MT-Bench79.078.0
Qwen 2.5 7B
MMLU
74.2
HumanEval
42.8
GSM8K
82.0
MT-Bench
79.0
Llama 3.1 8B
MMLU
69.4
HumanEval
40.2
GSM8K
79.6
MT-Bench
78.0
Capabilities
FeatureQwen 2.5 7BLlama 3.1 8B
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 8B)
$0.08/M
Input: $0.05/M
| Provider | Qwen 2.5 7B In $/M | Out $/M | Llama 3.1 8B In $/M | Out $/M |
|---|---|---|---|---|
| groq | — | — | $0.05 | $0.08 |
| together | $0.20 | $0.20 | $0.18 | $0.18 |
| fireworks | $0.20 | $0.20 | $0.20 | $0.20 |
Recommendation Summary
- ‣Qwen 2.5 7B scores higher on overall quality (70 vs 65).
- ‣Llama 3.1 8B is cheaper per output token ($0.08/M vs $0.20/M).
- ‣Qwen 2.5 7B has a smaller memory footprint (15.2 GB vs 16.1 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Qwen 2.5 7B is stronger at code generation (HumanEval: 42.8 vs 40.2).
- ‣Qwen 2.5 7B is better at math reasoning (GSM8K: 82.0 vs 79.6).