Knowledge Distillation vs Fine-Tuning for AI Agents: Which Path Delivers Opus-Grade Quality at SLM Cost?
By NeoSmith AI Research Team. March 20, 2026. 14 min read.
Knowledge distillation and supervised fine-tuning both adapt a model to your domain, but they start from different places and buy you different things. Distillation transfers the behaviour of a larger teacher model into a smaller student. Fine-tuning adjusts an existing model's weights on labelled examples of the task.
Key Takeaways
- Distillation wins when you need frontier-grade judgement at small-model cost and latency
- Fine-tuning wins when the task is narrow, well-labelled, and close to the base model's existing behaviour
- Training data requirements, compute cost, and serving latency differ sharply between the two paths