Qwen 2.5 Coder 7B vs DeepSeek Coder 6.7B
Architecture Comparison
SpecQwen 2.5 Coder 7BDeepSeek Coder 6.7B
TypeDENSEDENSE
Total Parameters7.6B6.7B
Active Parameters7.6B6.7B
Layers2832
Hidden Dimension3,5844,096
Attention Heads2832
KV Heads432
Context Length131,07216,384
Precision (default)BF16BF16
Memory Requirements
PrecisionQwen 2.5 Coder 7BDeepSeek Coder 6.7B
BF16 Weights15.2 GB13.4 GB
FP8 Weights7.6 GB6.7 GB
INT4 Weights3.8 GB3.4 GB
KV-Cache / Token57344 B524288 B
Activation Estimate0.80 GB0.80 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU
Capabilities
FeatureQwen 2.5 Coder 7BDeepSeek Coder 6.7B
Tool Use✗ No✗ No
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✗ No
Multilingual✗ No✗ No
Structured Output✓ Yes✗ No
API Pricing Comparison
Cheapest Output (Qwen 2.5 Coder 7B)
$0.20/M
Input: $0.20/M
Cheapest Output (DeepSeek Coder 6.7B)
$0.20/M
Input: $0.20/M
| Provider | Qwen 2.5 Coder 7B In $/M | Out $/M | DeepSeek Coder 6.7B In $/M | Out $/M |
|---|---|---|---|---|
| together | $0.20 | $0.20 | $0.20 | $0.20 |
Recommendation Summary
- ‣DeepSeek Coder 6.7B has a smaller memory footprint (13.4 GB vs 15.2 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Qwen 2.5 Coder 7B supports a longer context window (131,072 vs 16,384 tokens).
Compare Other Models
Qwen 2.5 Coder 7B vs DeepSeek R1→Qwen 2.5 Coder 7B vs DeepSeek V3→Qwen 2.5 Coder 7B vs Gemma 3 27B→Qwen 2.5 Coder 7B vs Llama 3.1 405B→Qwen 2.5 Coder 7B vs Llama 3.1 70B→Qwen 2.5 Coder 7B vs Llama 3.1 8B→DeepSeek Coder 6.7B vs DeepSeek R1→DeepSeek Coder 6.7B vs DeepSeek V3→DeepSeek Coder 6.7B vs Gemma 3 27B→DeepSeek Coder 6.7B vs Llama 3.1 405B→