Qwen 2.5 Coder 14B vs StarCoder2 15B
Architecture Comparison
SpecQwen 2.5 Coder 14BStarCoder2 15B
TypeDENSEDENSE
Total Parameters14.7B15.5B
Active Parameters14.7B15.5B
Layers4840
Hidden Dimension5,1206,144
Attention Heads4048
KV Heads84
Context Length131,07216,384
Precision (default)BF16BF16
Memory Requirements
PrecisionQwen 2.5 Coder 14BStarCoder2 15B
BF16 Weights29.4 GB31.0 GB
FP8 Weights14.7 GB15.5 GB
INT4 Weights7.3 GB7.8 GB
KV-Cache / Token196608 B81920 B
Activation Estimate1.20 GB1.50 GB
Minimum GPUs Needed (BF16)
H100 SXM1 GPU1 GPU
L40S1 GPU1 GPU
Quality Benchmarks
BenchmarkQwen 2.5 Coder 14BStarCoder2 15B
Overall5042
MMLUN/A45.0
HumanEvalN/A46.0
GSM8KN/A32.0
MT-BenchN/A58.0
Qwen 2.5 Coder 14B
StarCoder2 15B
MMLU
45.0
HumanEval
46.0
GSM8K
32.0
MT-Bench
58.0
Capabilities
FeatureQwen 2.5 Coder 14BStarCoder2 15B
Tool Use✗ No✗ No
Vision✗ No✗ No
Code✓ Yes✓ Yes
Math✓ Yes✗ No
Reasoning✗ No✗ No
Multilingual✗ No✗ No
Structured Output✓ Yes✗ No
API Pricing Comparison
Cheapest Output (Qwen 2.5 Coder 14B)
$0.30/M
Input: $0.30/M
Cheapest Output (StarCoder2 15B)
$0.30/M
Input: $0.30/M
| Provider | Qwen 2.5 Coder 14B In $/M | Out $/M | StarCoder2 15B In $/M | Out $/M |
|---|---|---|---|---|
| together | $0.30 | $0.30 | — | — |
| huggingface | — | — | $0.30 | $0.30 |
Recommendation Summary
- ‣Qwen 2.5 Coder 14B scores higher on overall quality (50 vs 42).
- ‣Qwen 2.5 Coder 14B has a smaller memory footprint (29.4 GB vs 31.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Qwen 2.5 Coder 14B supports a longer context window (131,072 vs 16,384 tokens).
Compare Other Models
Qwen 2.5 Coder 14B vs DeepSeek R1→Qwen 2.5 Coder 14B vs DeepSeek V3→Qwen 2.5 Coder 14B vs Gemma 3 27B→Qwen 2.5 Coder 14B vs Llama 3.1 405B→Qwen 2.5 Coder 14B vs Llama 3.1 70B→Qwen 2.5 Coder 14B vs Llama 3.1 8B→StarCoder2 15B vs DeepSeek R1→StarCoder2 15B vs DeepSeek V3→StarCoder2 15B vs Gemma 3 27B→StarCoder2 15B vs Llama 3.1 405B→