Models

TL;DR Between late June and late September I moved my daily setup through Sonnet 5, Opus 5, Fable 5.1, Opus 5.5 and Sonnet 5.5. Haiku stayed at 4.5 the whole time. I never saw a Haiku 5 in any log. There is no controlled benchmark in anything I kept. What follows is operational evidence: what I changed, what broke, what I verified, and what I would have gotten wrong if I had trusted what a model told me about itself. The changes that mattered were small: a per-model effort block in settings.json and a subagent default model. The expensive lessons were two traps. One is a bug where background-job subagents silently ran Haiku 4.5. The other is that a worker’s report of its own model cannot be believed. If you want the earlier, more general version of this, see re-tuning my setup for a new Opus model. This is what the next five releases taught me. The ladder These are first-seen dates from my own records: commit trailers in my repositories and, where I still have them, session transcripts. They are when I started using each model, not necessarily when it became available. ...

TL;DR On July 31 a fleet of long-running Claude manager sessions that I was using to supervise parallel work collapsed badly enough that I had to cold-start the whole thing. My first hypothesis was that the supervisor model was not smart enough. That hypothesis was checkable, and false. The transcripts showed the managers really were running Opus 5. They still failed. The dominant failure markers in their ledgers were stale and duplicate, which are bookkeeping problems that a script gets right every time and a model gets right most of the time. The same week, a Sonnet auditor at medium effort, given an explicit list of gates to check, found two real defects that the Opus supervisor had missed. The Opus supervisor was better at something else: writing the gate list and noticing what was not on it. I turned that into a table that allocates model and effort per type of turn, with scripts doing the work that is decidable. I am going to be careful about how much that one afternoon proves, which is not much. It is one observation, and I did not run a benchmark. I wrote earlier about sending finished code to a rival model for review. This is the same instinct from the other direction: reviewer and author should not share a blind spot, but they also do not need to share a price tier. ...

TL;DR Stable Diffusion 3.5 Large is 15 GB. RealVisXL is 6.5 GB. Throw in a few LoRAs and a VAE, and your SSD hits the wall fast. I run a MinIO bucket as the long-tail model store, sync it to a local overflow directory on a 30-minute schedule via rclone, and register both the hot (SSD) and cold (synced overflow) paths in ComfyUI’s extra_model_paths.yaml. Models appear transparently; the loader searches both tiers. A fresh model lands in MinIO, appears locally within 30 minutes, and ComfyUI finds it without any manual shuffling. ...