This issue explores trade-offs between model size, reasoning mode, tool-calling, and prompt structure in data science workflows. Contributors should analyse how different prompting approaches affect token usage, runtime, computational cost, and output quality. Submissions should include measurable outcomes rather than subjective impressions.