Not a typical “omg the model changed” post, so don’t even. Let me start by saying I am a power user with two max 20 accounts and have been day & night since late July of last year. I know the models and the CC harness very well. I know what to expect on each session, have religious workflows that only call for 2-3 turns per session, and will on occasion terminate sessions if I notice abnormalities. I have made a tremendous effort to make my CC experience as deterministic as possible and this allows me a frame of reference for slight behavioral changes. Sometime on Sunday Opus 4.7 OR the Harness (there wasn’t an update, I don’t think), model behavior changed. I only use Opus 4.7, so I can’t speak to other models. Let me give a few examples.
- I have a highly repetitive onboarding process that calls two skills. This is usually my first turn. Typically, the model calls both skills, thinks, reports back as ready. Since Sunday, the model calls the first skill, thinks, repeats for the second skill. This now takes roughly 3x the amount of time.
- Typically, each of my tasks after onboarding take 20-40 minutes. Today, each main task is on average 1 hour.
- The model will progress through a task, and I have noticed very long thinking blocks that seem out of place. The amount of thinking does not align with the actual required thinking amount at that time and place. There are other small behavioral aspects that are occurring that I won’t get into too much. Some of these things includes violating well established rules, such as ASCII only, a rule that has been followed effortlessly historically. I am unsure what the smaller behavioral changes indicate exactly, but from my perspective, as for the time increases, I believe it is possible that we are seeing a compute re-distribution queue system, where the user is entered into a queue and the extended thinking masks the queue. Since thinking blocks are now hidden, I have no way of actually verifying this. I don’t know if this is true, but it certainly feels as though this is happening. Now to argue against my theory - which makes things even more unusual. Typically my sessions end at 250-400k context. Since I only do 1-3 turns, this is okay. However, more intensive sessions that would end on the higher end, maybe around 450-500k, are now ending around 700k-800k. This is unusual. Highly unusual. Just in the past few days I reached beyond 600k for the first time ever, and now it has been happening consistently. If there is some sort of queue, is the model still thinking in some way? If there is not, does this mean the model is now less efficient? Why would the model be less efficient suddenly? OR, is the model more efficient and performing better due-diligence? Personally, I don’t see any “better” results. If anything, the longer sessions bring more poor behavior which is evidenced in some actions such as abnormal dev log formatting (we have a well established format, I watched this get violated for the first time yesterday during a session that passed 800k). Something odd is occurring. I’m wondering, what has your experience been since Sunday? It’s possible that the audience that can answer this question in the capacity that it’s being asked is on the lower end of things. Really looking to hear from users with similar religious workflows and mature workspaces & codebases. submitted by /u/Ambitious_Injury_783
Originally posted by u/Ambitious_Injury_783 on r/ClaudeCode