Qwen 2.5 VL 7B vs Qwen 2.5 VL 72B
Architecture Comparison
SpecQwen 2.5 VL 7BQwen 2.5 VL 72B
TypeDENSEDENSE
Total Parameters7.6B72.7B
Active Parameters7.6B72.7B
Layers2880
Hidden Dimension3,5848,192
Attention Heads2864
KV Heads48
Context Length131,072131,072
Precision (default)BF16BF16
Memory Requirements
PrecisionQwen 2.5 VL 7BQwen 2.5 VL 72B
BF16 Weights15.2 GB145.4 GB
FP8 Weights7.6 GB72.7 GB
INT4 Weights3.8 GB36.4 GB
KV-Cache / Token57344 B327680 B
Activation Estimate0.80 GB3.00 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU3 GPUs
L40S1 GPU4 GPUs
Capabilities
FeatureQwen 2.5 VL 7BQwen 2.5 VL 72B
Tool Use✓ Yes✓ Yes
Vision✓ Yes✓ Yes
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (Qwen 2.5 VL 7B)
$0.20/M
Input: $0.20/M
Cheapest Output (Qwen 2.5 VL 72B)
$0.90/M
Input: $0.90/M
| Provider | Qwen 2.5 VL 7B In $/M | Out $/M | Qwen 2.5 VL 72B In $/M | Out $/M |
|---|---|---|---|---|
| together | $0.20 | $0.20 | $0.90 | $0.90 |
Recommendation Summary
- ‣Qwen 2.5 VL 7B is cheaper per output token ($0.20/M vs $0.90/M).
- ‣Qwen 2.5 VL 7B has a smaller memory footprint (15.2 GB vs 145.4 GB BF16), making it easier to deploy on fewer GPUs.
Compare Other Models
Qwen 2.5 VL 7B vs DeepSeek R1→Qwen 2.5 VL 7B vs DeepSeek V3→Qwen 2.5 VL 7B vs Gemma 3 27B→Qwen 2.5 VL 7B vs Llama 3.1 405B→Qwen 2.5 VL 7B vs Llama 3.1 70B→Qwen 2.5 VL 7B vs Llama 3.1 8B→Qwen 2.5 VL 72B vs DeepSeek R1→Qwen 2.5 VL 72B vs DeepSeek V3→Qwen 2.5 VL 72B vs Gemma 3 27B→Qwen 2.5 VL 72B vs Llama 3.1 405B→