Qwen 2.5 7B vs Gemma 2 9B
Architecture Comparison
SpecQwen 2.5 7BGemma 2 9B
TypeDENSEDENSE
Total Parameters7.6B9.2B
Active Parameters7.6B9.2B
Layers2842
Hidden Dimension3,5843,584
Attention Heads2816
KV Heads48
Context Length131,0728,192
Precision (default)BF16BF16
Memory Requirements
PrecisionQwen 2.5 7BGemma 2 9B
BF16 Weights15.2 GB18.4 GB
FP8 Weights7.6 GB9.2 GB
INT4 Weights3.8 GB4.6 GB
KV-Cache / Token57344 B344064 B
Activation Estimate1.00 GB1.00 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU
Quality Benchmarks
BenchmarkQwen 2.5 7BGemma 2 9B
Overall7068
MMLU74.271.3
HumanEval42.840.0
GSM8K82.076.0
MT-Bench79.078.0
Qwen 2.5 7B
MMLU
74.2
HumanEval
42.8
GSM8K
82.0
MT-Bench
79.0
Gemma 2 9B
MMLU
71.3
HumanEval
40.0
GSM8K
76.0
MT-Bench
78.0
Capabilities
FeatureQwen 2.5 7BGemma 2 9B
Tool Use✓ Yes✗ No
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 (Gemma 2 9B)
$0.10/M
Input: $0.10/M
| Provider | Qwen 2.5 7B In $/M | Out $/M | Gemma 2 9B In $/M | Out $/M |
|---|---|---|---|---|
| deepinfra | — | — | $0.10 | $0.10 |
| together | $0.20 | $0.20 | $0.20 | $0.20 |
| fireworks | $0.20 | $0.20 | — | — |
Recommendation Summary
- ‣Qwen 2.5 7B scores higher on overall quality (70 vs 68).
- ‣Gemma 2 9B is cheaper per output token ($0.10/M vs $0.20/M).
- ‣Qwen 2.5 7B has a smaller memory footprint (15.2 GB vs 18.4 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Qwen 2.5 7B supports a longer context window (131,072 vs 8,192 tokens).
- ‣Qwen 2.5 7B is stronger at code generation (HumanEval: 42.8 vs 40.0).
- ‣Qwen 2.5 7B is better at math reasoning (GSM8K: 82.0 vs 76.0).