Model Distillation
A technique where a smaller, faster AI model (the student) is trained to replicate the behavior of a larger, more capable model (the teacher). Distillation transfers the teacher's knowledge into a compact model that's cheaper and faster to run while retaining most of the performance. For AI agents, distillation enables deploying capable models on edge devices, reducing inference costs at scale, and meeting latency requirements that large models can't hit. OpenAI, Anthropic, and Google all offer distilled model variants.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Example
A support agent running on GPT-4 costs $0.12 per interaction. After distilling GPT-4's behavior into a fine-tuned GPT-4o-mini, the same agent achieves 92% of the quality at $0.01 per interaction—a 12x cost reduction.
Frequently asked questions
- When should you distill vs. just use a smaller model?
- Distill when: you have a working large-model agent with good performance and need to reduce cost/latency, or when you need the model to handle domain-specific patterns the base small model doesn't. Use a smaller model directly when: the task is simple enough that the base small model handles it well, or when you don't have the data and infrastructure for distillation.