Anthropic released Claude Opus 5.5 on Tuesday 22 September, OpenAI launched GPT-6 Sol and Luna a few minutes later. Both had called, a few days earlier, for a slower race. They did the opposite, on the same day, by cutting their prices.
Opus 5.5 offers the level of the old top tier for roughly 40% less to run than Opus 5; the new GPT-6 models are built to cost less. Grok 4.7 came out the day before, less convincing but cheaper. The token price collapses as quality rises, in cycles of a few weeks.
This pace changes the calculation for a French organisation. The model you choose today will be outclassed within a quarter, and cheaper the month after. A task too costly to automate at scale a year ago becomes affordable before the end of the financial year. The constraint moves from price to the ability to keep up with the tempo.
Two moves follow. The first concerns skills. Train your teams on what survives a change of model: framing a problem, writing a clear instruction, checking an output, judging whether a tool fits a task. That know-how holds from one version to the next; mastery of a given model's menus expires with it.
The second concerns architecture and procurement. Build your projects to switch models without rebuilding everything: an abstraction layer between your applications and the provider, a contract that does not lock you in, a test bench you replay each quarter on your own cases.
The day a model twice as cheap reaches your quality level, you must be able to switch in a day, not in six months.
The call to slow down will stay a dead letter as long as a competitor can take the vacated place by shipping on the same day. The race will not slow. What you control is what, in your own house, does not depend on the model of the month: people who know how to use it and plumbing you can replace painlessly.
The lasting value sits there, while the rest expires in a few weeks. The Masteria editorial team.