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Model Distillation

Also known as: knowledge distillation, distillation, distilled model.

Last updated: May 24, 2026

What is Model Distillation?

Definition

Model distillation is a technique for training a small, fast 'student' model to imitate a large, expensive 'teacher' model — transferring most of the teacher's capability into a fraction of the size. It's how providers ship cheap, quick models like Claude Haiku, Gemini Flash, and GPT mini that punch above their parameter count.

In plain English

Training a small model from scratch on raw text produces a weak model. Distillation does better: you run a large, capable teacher model and train the student to reproduce not just the teacher's answers but the full pattern of probabilities behind them, which carries far richer signal than hard labels alone. The student ends up smaller, cheaper to run, and much faster, while retaining a surprising share of the teacher's quality on most tasks. This is the engine behind the tiered model families every provider now ships — a flagship for hard problems and distilled siblings for high-volume, latency-sensitive, cost-sensitive work. Distilled models aren't magic: they lose some depth on the hardest reasoning and long-tail knowledge, which is exactly why frontier models still exist. The practical upshot is a spectrum of price and speed, and picking the right rung per task is where real savings come from.

Example

A chat product uses a frontier model to draft answers during design, then distills that behavior into a small student model for production. The distilled model handles 90% of live traffic — greetings, FAQs, simple lookups — at a tenth of the cost and a fraction of the latency, while the flagship is reserved for the gnarly 10%. Users notice nothing except faster replies; the finance team notices a much smaller bill.

Model Distillation vs Fine-Tuning

Distillation and fine-tuning both adapt models but for different ends. Distillation compresses a big model into a smaller, faster one that mimics it — the goal is efficiency. Fine-tuning specializes a model on a narrow task or style — the goal is customization. Distillation changes a model's size; fine-tuning changes its behavior. Providers distill to build cheap model tiers; teams fine-tune to fit their use case.

Model Distillation in Vincony

Vincony's catalog spans the full spectrum from frontier models to their distilled, budget siblings — Claude Haiku, Gemini Flash, GPT mini, DeepSeek — and Smart Routing sends each task to the smallest model that still nails it, so you capture distillation's savings automatically.

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Related terms & guides

Fine-tuningInferenceModel routingLatencyLarge language model
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