Qwen 2.5 Coder 7B vs Code Llama 7B
Architecture Comparison
SpecQwen 2.5 Coder 7BCode Llama 7B
TypeDENSEDENSE
Total Parameters7.6B7B
Active Parameters7.6B7B
Layers2832
Hidden Dimension3,5844,096
Attention Heads2832
KV Heads432
Context Length131,07216,384
Precision (default)BF16BF16
Memory Requirements
PrecisionQwen 2.5 Coder 7BCode Llama 7B
BF16 Weights15.2 GB14.0 GB
FP8 Weights7.6 GB7.0 GB
INT4 Weights3.8 GB3.5 GB
KV-Cache / Token57344 B524288 B
Activation Estimate0.80 GB1.00 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU
Quality Benchmarks
BenchmarkQwen 2.5 Coder 7BCode Llama 7B
Overall5039
MMLUN/A42.0
HumanEvalN/A31.0
GSM8KN/A28.0
MT-BenchN/A60.0
Qwen 2.5 Coder 7B
Code Llama 7B
MMLU
42.0
HumanEval
31.0
GSM8K
28.0
MT-Bench
60.0
Capabilities
FeatureQwen 2.5 Coder 7BCode Llama 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 (Code Llama 7B)
$0.20/M
Input: $0.20/M
| Provider | Qwen 2.5 Coder 7B In $/M | Out $/M | Code Llama 7B In $/M | Out $/M |
|---|---|---|---|---|
| together | $0.20 | $0.20 | $0.20 | $0.20 |
Recommendation Summary
- ‣Qwen 2.5 Coder 7B scores higher on overall quality (50 vs 39).
- ‣Code Llama 7B has a smaller memory footprint (14.0 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→Code Llama 7B vs DeepSeek R1→Code Llama 7B vs DeepSeek V3→Code Llama 7B vs Gemma 3 27B→Code Llama 7B vs Llama 3.1 405B→