Llama 3.1 8B vs Qwen 2.5 7B
Architecture Comparison
SpecLlama 3.1 8BQwen 2.5 7B
TypeDENSEDENSE
Total Parameters8.03B7.6B
Active Parameters8.03B7.6B
Layers3228
Hidden Dimension4,0963,584
Attention Heads3228
KV Heads84
Context Length131,072131,072
Precision (default)BF16BF16
Memory Requirements
PrecisionLlama 3.1 8BQwen 2.5 7B
BF16 Weights16.1 GB15.2 GB
FP8 Weights8.0 GB7.6 GB
INT4 Weights4.0 GB3.8 GB
KV-Cache / Token131072 B57344 B
Activation Estimate1.00 GB1.00 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU
Quality Benchmarks
BenchmarkLlama 3.1 8BQwen 2.5 7B
Overall6570
MMLU69.474.2
HumanEval40.242.8
GSM8K79.682.0
MT-Bench78.079.0
Llama 3.1 8B
MMLU
69.4
HumanEval
40.2
GSM8K
79.6
MT-Bench
78.0
Qwen 2.5 7B
MMLU
74.2
HumanEval
42.8
GSM8K
82.0
MT-Bench
79.0
Capabilities
FeatureLlama 3.1 8BQwen 2.5 7B
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 (Llama 3.1 8B)
$0.08/M
Input: $0.05/M
Cheapest Output (Qwen 2.5 7B)
$0.20/M
Input: $0.20/M
| Provider | Llama 3.1 8B In $/M | Out $/M | Qwen 2.5 7B In $/M | Out $/M |
|---|---|---|---|---|
| groq | $0.05 | $0.08 | — | — |
| together | $0.18 | $0.18 | $0.20 | $0.20 |
| 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).