AI Model Distillation: Teacher vs. Student Models

AI model distillation trains a smaller student model to reproduce a larger teacher model’s behavior for narrow tasks, using soft probability outputs, generated responses, or more advanced methods such as logit matching, on-policy grading, and multi-teacher training. Unlike fine-tuning, quantization, and pruning, distillation changes the model being deployed and can be combined with those techniques to reduce cost, latency, and hardware requirements further. The discussion emphasizes that licensing is a central constraint, as many closed-model providers prohibit using their outputs to train competing models, while open-weight models can provide legal flexibility, direct access to logits, and predictable self-hosting costs. Distillation can offer substantial efficiency gains but also produces specialized models that may inherit teacher errors, degrade outside their training scope, and require ongoing evaluation and retraining. Before building a custom pipeline, teams are encouraged to test existing low-cost open models and use tiered routing, reserving larger models for harder requests; custom distillation is most justified at very high volumes, for stable and constrained tasks such as classification or extraction, or where strict latency, privacy, or deployment-location requirements rule out hosted models.

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