On July 7, Anthropic Claude Code team member Lydia Hallie wrote a long post, for the first time fully laying out the underlying mechanism of the Model and Effort two sliders. Model decides "can it", Effort decides "is it willing to do its full work". Switching Model is essentially swapping a set of frozen weights: after training ends, billions of parameters become read-only, and neither the prompt nor CLAUDE.md can change it. Effort controls how much work Claude puts into a single task — how many files to read, whether to run tests, whether to push a multi-step task all the way through and come back. The official illustration: on the same prompt, the high-Effort path generates about 7× the tokens of the low-Effort one, all the extras spent on verification. The post uses three analogies: Sonnet is an all-rounder who gives you an entire afternoon, Fable is a specialist who can spot what others miss at a glance, Opus is a veteran expert with only 5 minutes. The conclusion is counter-intuitive: a mid-tier model with high Effort can often beat a flagship model on low Effort. The real culprit behind March's "got dumber" storm. On March 4, Anthropic changed the Effort default from high to medium to control latency, the changelog wrote it but no one paid attention. AMD AI head Stella Laurenzo flipped through 6,852 session logs, measured a 67% drop in thinking amount compared to February, and dropped "Claude can no longer be trusted to do complex engineering work". It wasn't reverted until a month later. Most people are still stuck on the old thinking of "switch to a bigger model", completely unaware of the Effort switch sitting right next to them. So what. On the surface, the post is about parameter tuning; behind it is a paradigm shift: AI programming competition is shifting from "whose model is stronger" to "who is better at scheduling". Simple changes use Sonnet at a low tier, large refactors use Opus at a high tier, long-running Agent tasks use Fable with full Effort. The new ultracode tier in Claude Code is essentially writing this scheduling into the product — select it, and Claude itself will judge whether to spin up sub-Agent teams to work in parallel. The most central action, "pick a model", is being split into two independent dimensions: "how much money × how much work"; the next watershed will appear at whichever Agent framework automates this scheduling first.