A common trap: labeling models "good" or "bad" in isolation. Performance is model + dataset + objective . Like a Ferrari excelling on racetracks but flopping off-road — context dictates everything. Key dimensions to benchmark: Customization level (prompt tuning vs. full retraining) Model size (parameter count vs. inference efficiency) Context window (how much history it retains) Latency (critical for real-time apps) Licensing (commercial restrictions) Deployment (API vs