This research challenges the conventional wisdom that bigger models are always better, demonstrating that moderate-sized, domain-specialized models can outperform much larger general-purpose models in software engineering tasks.

• Medium models (350M parameters) with domain focus achieve superior performance on code labeling tasks compared to models 10-100x larger
• Specialized models trained on StackOverflow data showed significant improvements in task-specific accuracy
• Domain adaptation through continued pretraining provides substantial benefits without requiring massive resources
• These findings suggest more cost-effective and accessible AI solutions for software engineering teams

For engineering teams, this research offers a practical path to implementing powerful code-related AI tools without the computational and financial burden of large language models.

Skill over Scale: The Case for Medium, Domain-Specific Models for SE