We've been investigating these reports, and a few of the top issues we've found are:
1. Prompt cache misses when using 1M token context window are expensive. Since Claude Code uses a 1 hour prompt cache window for the main agent, if you leave your computer for over an hour then continue a stale session, it's often a full cache miss. To improve this, we have shipped a few UX improvements (eg. to nudge you to /clear before continuing a long stale session), and are investigating defaulting to 400k context instead, with an option to configure your context window to up to 1M if preferred. To experiment with this now, try: CLAUDE_CODE_AUTO_COMPACT_WINDOW=400000 claude.
2. People pulling in a large number of skills, or running many agents or background automations, which sometimes happens when using a large number of plugins. This was the case for a surprisingly large number of users, and we are actively working on (a) improving the UX to make these cases more visible to users and (b) more intelligently truncating, pruning, and scheduling non-main tasks to avoid surprise token usage.
In the process, we ruled out a large number of hypotheses: adaptive thinking, other kinds of harness regressions, model and inference regressions.
We are continuing to investigate and prioritize this. The most actionable thing for people running into this is to run /feedback, and optionally post the feedback ids either here or in the Github issue. That makes it possible for us to debug specific reports.
Hey all, Boris from the Claude Code team here. I just responded on the issue, and cross-posting here for input.
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Hi, thanks for the detailed analysis. Before I keep going, I wanted to say I appreciate the depth of thinking & care that went into this.
There's a lot here, I will try to break it down a bit. These are the two core things happening:
> `redact-thinking-2026-02-12`
This beta header hides thinking from the UI, since most people don't look at it. It *does not* impact thinking itself, nor does it impact thinking budgets or the way extended reasoning works under the hood. It is a UI-only change.
Under the hood, by setting this header we avoid needing thinking summaries, which reduces latency. You can opt out of it with `showThinkingSummaries: true` in your settings.json (see [docs](https://code.claude.com/docs/en/settings#available-settings)).
If you are analyzing locally stored transcripts, you wouldn't see raw thinking stored when this header is set, which is likely influencing the analysis. When Claude sees lack of thinking in transcripts for this analysis, it may not realize that the thinking is still there, and is simply not user-facing.
> Thinking depth had already dropped ~67% by late February
We landed two changes in Feb that would have impacted this. We evaluated both carefully:
1/ Opus 4.6 launch → adaptive thinking default (Feb 9)
Opus 4.6 supports adaptive thinking, which is different from thinking budgets that we used to support. In this mode, the model decides how long to think for, which tends to work better than fixed thinking budgets across the board. `CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING` to opt out.
2/ Medium effort (85) default on Opus 4.6 (Mar 3)
We found that effort=85 was a sweet spot on the intelligence-latency/cost curve for most users, improving token efficiency while reducing latency. On of our product principles is to avoid changing settings on users' behalf, and ideally we would have set effort=85 from the start. We felt this was an important setting to change, so our approach was to:
1. Roll it out with a dialog so users are aware of the change and have a chance to opt out
2. Show the effort the first few times you opened Claude Code, so it wasn't surprising.
Some people want the model to think for longer, even if it takes more time and tokens. To improve intelligence more, set effort=high via `/effort` or in your settings.json. This setting is sticky across sessions, and can be shared among users. You can also use the ULTRATHINK keyword to use high effort for a single turn, or set `/effort max` to use even higher effort for the rest of the conversation.
Going forward, we will test defaulting Teams and Enterprise users to high effort, to benefit from extended thinking even if it comes at the cost of additional tokens & latency. This default is configurable in exactly the same way, via `/effort` and settings.json.
We've been investigating these reports, and a few of the top issues we've found are:
1. Prompt cache misses when using 1M token context window are expensive. Since Claude Code uses a 1 hour prompt cache window for the main agent, if you leave your computer for over an hour then continue a stale session, it's often a full cache miss. To improve this, we have shipped a few UX improvements (eg. to nudge you to /clear before continuing a long stale session), and are investigating defaulting to 400k context instead, with an option to configure your context window to up to 1M if preferred. To experiment with this now, try: CLAUDE_CODE_AUTO_COMPACT_WINDOW=400000 claude.
2. People pulling in a large number of skills, or running many agents or background automations, which sometimes happens when using a large number of plugins. This was the case for a surprisingly large number of users, and we are actively working on (a) improving the UX to make these cases more visible to users and (b) more intelligently truncating, pruning, and scheduling non-main tasks to avoid surprise token usage.
In the process, we ruled out a large number of hypotheses: adaptive thinking, other kinds of harness regressions, model and inference regressions.
We are continuing to investigate and prioritize this. The most actionable thing for people running into this is to run /feedback, and optionally post the feedback ids either here or in the Github issue. That makes it possible for us to debug specific reports.