A few weeks ago we argued that AI compute had started to behave like a commodity, with a cheap open tier splitting away from an expensive frontier. That piece explained the mechanics. The obvious next question was the practical one: what does a real piece of knowledge work actually cost once you stop looking solely at tokens and start measuring business outcomes, and is the cheaper tier competitive?
So we measured one. We took a single complex task, an RFP response pipeline that reads a tender, selects credentials, drafts a reply and renders a branded deck, and ran it end to end. Then we costed that exact task across the main ways you can currently buy AI: the frontier APIs, open-weight models, self-hosted clusters, local machines, the flagship subscriptions, and Microsoft's Copilot Cowork.
The same work came out at 16 cents at the cheap end, on a well-used self-hosted cluster, and $27.77 at the expensive end. A spread of more than 170x, for identical output. The price you pay now depends far less on the intelligence applied than on the purchasing model wrapped around it.
Two findings stand out, and we flagged both last week before we had the numbers. The first is Copilot Cowork, which we said looked prohibitively expensive. It is. Microsoft prices Cowork in credits at one cent each, grading tasks light, medium or heavy, which suggests a ceiling around $12. Our real RFP burned 2,777 credits, or $27.77, more than double that heavy figure. The telling detail is that Cowork ran on Sonnet, the same model that costs $8.10 through the raw API. So the threefold gap is a clean reading of the managed-agent wrapper, not a difference in intelligence. That premium buys governance and Microsoft 365 grounding, but anyone budgeting Cowork should price it from measured runs, not from the headline task bands. On heavy work, those bands badly understate what you will actually spend.
The second is GLM-5.2, which we said was bringing near-frontier performance to a reasonable price point. Now quantified, it lands at an intelligence score just within touching distance of the best closed models, at barely $2 per million tokens. What makes it different is that it arrives in three forms at once: a cheap metered API, a cheap subscription, and openly licensed weights you can host yourself. A year ago, choosing sovereignty or cost control meant accepting a real capability penalty. That penalty has largely gone. Run GLM-5.2 on a well-used cluster and the same RFP task costs around 16 cents, because once the hardware is paid for the marginal cost is essentially electricity.
The full analysis covers the building blocks of AI pricing, the seven provider tiers, the cache economics behind every agentic task, and the complete cost table for June 2026.
Read the full guide: AI Cost Analysis, June 2026 →
Takeaways: The same task can cost 16 cents or nearly $28 depending entirely on how you buy it. Copilot Cowork sits alone, for now prohibitively expensive, while GLM-5.2 now delivers near-frontier work for the price of a coffee. The organisations that handle the next eighteen months well will not pick a single provider. They will treat AI as procurement, route each task to the cheapest tier that can do it well, and explore new ways to host and run their own inference.