How Small AI Models Get So Good

Most people who use an AI assistant every day have never heard the word "distillation," and yet it shapes nearly everything they pay for. The fast, affordable models that answer customer emails, draft code, and summarize documents are rarely the giant frontier systems that make headlines. They are smaller students, trained to imitate a much larger teacher. That handoff of capability from big to small is knowledge distillation, and until recently there was no single place to understand it as a whole. Global Distillation, at global-distillation.com, fills that gap with unusual care.

Distillation is a research technique, but it is also a pricing strategy, a contract clause, a national security question, and a practical engineering task. The site treats each of those as a legitimate lens and organizes itself around eight perspectives: academic, financial, political, company, developer, customer, a method library, and a timeline. A policy analyst and a machine learning engineer can arrive at the same homepage and each find a page written with their questions in mind.

The academic perspective traces the idea back to its roots. The timeline opens in August 2006 with the "Model Compression" paper by Bucila, Caruana, and Niculescu-Mizil, and runs through 130 dated events to the present. Reading it in order, one watches an obscure research idea turn into a product strategy, then a market shock, and finally a matter for governments. The site notes that policy and legal events cluster almost entirely in the last two years, which tells its own story about how quickly the ground has shifted.

Choose a model

The customer page may be the most immediately useful section for anyone on the buying side of AI. It reframes the usual question of whether a small model is "good enough" into something more answerable: which tier, for which workload, under what license, and from which vendor. The site reports that small models within a single generation retain roughly 88 to 98 percent of frontier performance on knowledge benchmarks, while long-horizon coding and tool-use tasks show a steeper drop. That distinction matters enormously when deciding where premium pricing is justified and where it is simply waste.

The licensing discussion is equally practical. The site lays out which providers prohibit distilling their outputs into custom models and which open-weight licenses permit it, and it makes the sensible observation that having a self-hosting alternative gives buyers real negotiating leverage when API prices move. And prices do move. The site's enterprise inference index stood at $1.17 per million tokens in September 2026, down 43 percent in a matter of months, which is precisely the sort of figure a procurement team wants to have on hand before signing a long contract.

You can explore it at global-distillation.com.