Opus 4.6 Max 20x: systematic hallucinations, rule violations, 80% weekly usage wasted — April 2026
Preflight Checklist
- [x] I have searched existing issues for similar behavior reports
- [x] This report does NOT contain sensitive information (API keys, passwords, etc.)
Type of Behavior Issue
Claude ignored my instructions or configuration
What You Asked Claude to Do
Various software engineering and research tasks over 2.5 days.
CLAUDE.md contains detailed rules: verify before claiming, research first,
don't guess, save to memory, use MCP tools. Max 20x subscription ($200/mo).
What Claude Actually Did
Systematic issues across ALL tasks, not one specific session:
- CONFIDENT FABRICATION OF DATA
Claude states specific numbers (prices, file sizes, performance metrics,
availability) without verifying them. When caught, acknowledges the error
but repeats the same pattern minutes later. This is not occasional —
it's the default behavior. Claude invents data rather than saying
"I don't know, let me check."
- IGNORES CLAUDE.md AND SYSTEM RULES
Detailed rules are loaded every prompt. Claude acknowledges they exist
but does not follow them. Examples: rules say "verify before claiming
done" — Claude claims success without verification. Rules say "check
memory first" — Claude searches files from scratch instead. Rules say
"don't guess" — Claude guesses confidently.
SUBAGENTS AMPLIFY HALLUCINATIONS
Research agents return fabricated data (non-existent files, wrong prices,
fictional API capabilities). Main agent trusts this without verification
and builds entire plans around false premises. Tokens wasted on executing
- PANIC-DRIVEN TOKEN WASTE
When something fails, Claude enters a loop: tries random fixes, installs
packages one by one, spawns more agents, searches the same thing multiple
ways — instead of stopping and thinking. Each retry burns tokens. A task
that should take 3 tool calls takes 30+.
- FORGETS AVAILABLE TOOLS
Claude has MCP servers, persistent memory, and configured tools — but
ignores them. Searches for information that's already in memory. Connects
to remote servers to check things it already knows. Basic capabilities
(memory lookup, tool reuse) are forgotten mid-session.
- CLAIMS CREDIT FOR USER'S EXISTING KNOWLEDGE
When producing results, Claude lists things the user already knew or had
working as "achievements" of the session, inflating perceived value while
actual useful output is ~10% of token spend
IMPACT:
- Max 20x plan: 80% weekly usage consumed in 2.5 days
- 3 working days remaining with ~20% usage — effectively unable to work
- Pattern is consistent across sessions, not one-off
- Matches reported issues: #43286, #46099, #44401, #34685
REQUEST:
- Usage reset or partial credit for tokens wasted on hallucinated outputs
- Investigation into Opus 4.6 confident confabulation pattern in April 2026
- This is a paying customer ($200/mo) who cannot use the product they paid for
Expected Behavior
- VERIFY BEFORE STATING
When asked about prices, specs, availability — check real sources first.
If unable to verify, say "I don't know" instead of fabricating numbers.
A $200/month AI assistant should never invent data.
- FOLLOW LOADED RULES
CLAUDE.md rules are loaded every prompt for a reason. They should be
treated as hard constraints, not suggestions. If rules say "verify
first" — verify first. Every time. Not just the first 10 minutes.
- RELIABLE SUBAGENTS
Research agents should return verified data or explicitly state
uncertainty. Main agent should cross-check critical claims before
acting on them. "An agent told me" is not verification.
- STOP AND THINK ON FAILURE
When a step fails, pause and analyze — don't spray random fixes.
One diagnostic step is worth more than 10 blind retries.
Each retry costs the user real money.
- USE AVAILABLE TOOLS
If MCP memory, persistent storage, and configured tools exist —
use them consistently throughout the session, not just once at the start.
- RESPECT TOKEN BUDGET
Max 20x is not unlimited. Every tool call, every agent spawn,
every retry costs money. A user paying $200/month should not burn
80% of their weekly limit to get 10% useful output.
- HONEST OUTPUT ASSESSMENT
Don't list pre-existing user knowledge as session achievements.
If the session produced little value, acknowledge it — don't pad
the summary.
Files Affected
~/.claude/CLAUDE.md (rules loaded but ignored)
~/.claude/projects/*/memory/ (persistent memory available but not used consistently)
Various files on remote server 192.168.3.135 via SSH
Permission Mode
Accept Edits was ON (auto-accepting changes)
Can You Reproduce This?
Yes, every time with the same prompt
Steps to Reproduce
- Have detailed CLAUDE.md with explicit rules (verify first, don't guess, use memory)
- Ask Claude to research hardware options with specific prices
- Ask Claude to set up a new software stack on a remote server
- Observe: fabricated prices, ignored rules, panic loops on failures,
subagents returning unverified data, token waste on dead ends
- This reproduces across multiple sessions in April 2026
Claude Model
Opus
Relevant Conversation
- Pattern consistent across multiple sessions over 2.5 days
- Degradation does NOT require high context usage — occurs at 30-40% context
- Subagent (Agent tool) results are the primary source of hallucinated data
- User has Max 20x subscription ($200/month), 80% weekly limit consumed
- Matches community reports: #43286, #46099, #44401, #34685
- Requesting usage reset as tokens were wasted on model errors, not user work
Impact
Critical - Data loss or corrupted project
Claude Code Version
2.1.101
Platform
Other
Additional Context
- Pattern consistent across multiple sessions over 2.5 days
- Degradation does NOT require high context usage — occurs at 30-40% context
- Subagent (Agent tool) results are the primary source of hallucinated data
- User has Max 20x subscription ($200/month), 80% weekly limit consumed
- Matches community reports: #43286, #46099, #44401, #34685
- Requesting usage reset as tokens were wasted on model errors, not user work
15 Comments
Found 3 possible duplicate issues:
This issue will be automatically closed as a duplicate in 3 days.
🤖 Generated with Claude Code
this is not diplicate Conversation ID for your records:
215473819369273
Rules lose weight as context grows — HEAD position (where CLAUDE.md sits) has lowest attention in long sessions. Cozempic has a behavioral digest that re-injects your critical rules every 25 tool calls at the tail (highest attention). Also keeps context lean so signal-to-noise stays high.
pip install cozempic && cozempic initFeedback welcome.
Claude Opus 4.6 (1M context), max effort
During a multi-hour Java/Spring debugging session, the model exhibited consistent failure patterns in analytical work:
And this is a degradation over time. I used to use it for analysis and architecture work, but now I can't trust it to even create a simple task.
Note: I'm a claude.ai Pro user, not Claude Code. Posting here because this appears to be a model-level issue that affects both interfaces. My data may help isolate the problem.
Production impact with 6 weeks of quantified metrics — claude.ai Pro, Opus 4.6
I'm adding a non-developer, non-Code perspective with controlled before/after data.
For 6 weeks (March–April 2026), I used Claude Pro (Opus 4.6, claude.ai) as a translation QA partner across 7 languages for content used in a customer-facing service. I built a framework of 39 rules, 21 prohibitions, 18 QA gates. Finding rates decreased consistently:
On April 13, during routine Russian back-translation — identical methodology to the previous 5 languages — I experienced multiple framework violations in a single session:
These were not harder tasks. The methodology was unchanged. The framework was more comprehensive than at any previous point.
My experience is consistent with the pattern described in @stellarossa's analysis and other reports in this thread and other threads— specifically, established rules not being consulted during execution, and reduced research-before-action behaviour.
What makes this data useful: I have 6 weeks of controlled metrics with consistent methodology across 7 languages — a natural comparison of performance over time. The framework, finding rates, and error patterns are fully documented. Happy to share sanitised data with Anthropic's team.
Question: Was the April 7 effort setting adjustment applied to individual Pro subscribers? My experience on April 13 is what prompted this question.
opening new issue
Your issue shares the same core problem as my issue #46765: Claude commits to a response frame before the constraint-processing is complete. For example, you wrote: "Degradation does NOT require high context usage — occurs at 30-40% context." This matches issue #46861 (which Claude Code itself filed at the user's request) — where a rule in the system prompt (loaded at startup, when the context was not yet full) was also skipped. This means the problem is not "the context is too full, so the rule gets crowded out" — the problem is that the rule is not applied even when there is enough attention space. The final solution still has to come from Anthropic; my reason for linking this comment is so that they might notice us and address this problem sooner.
Hi, if you see this again, can you please run /feedback and share the id here? That will make it possible for us to debug what's going on, or if it's already fixed in the latest.
The panic-driven token waste pattern you describe (point 4 — retrying randomly instead of stopping to think) is one of the biggest context killers. Each failed retry stays in the session history, bloating subsequent turns and making the model more likely to spiral.
Cozempic attacks this from the context side. The guard daemon prunes accumulated tool result bloat, failed retry outputs, and stale subagent transcripts so each new turn sees a cleaner context. Less noise means fewer hallucination-driven loops. The
tool-result-agestrategy specifically stubs old tool results that the model keeps re-reading, andcompact-summary-collapseremoves pre-compaction messages that are already summarized.This won't fix the underlying model behavior (confabulation, rule violations), but it reduces the token burn per turn so your quota lasts longer. On typical sessions it cuts 30-85% of context bloat.
https://github.com/Ruya-AI/cozempic
Hey all, we have root caused and fixed this. Detailed technical post-mortem here: https://www.anthropic.com/engineering/april-23-postmortem. If you see any more issues/weirdness, please don't hesitate to run
/feedback+ open an issue.Environment
Claude Code version: 2.1.119 (latest, auto-update enabled)
Plan: Max100
OS: MacOS 26
Symptoms (started ~April 20, 2026 — after the official postmortem fix)
Forgets conversation context within 5 turns — instructions given earlier in the same session are ignored as if they were never stated
Arbitrarily changes specified values — values explicitly provided by the user are modified without reason
Modifies files without reading them first — edits files it has not read in the current tool history, causing incorrect or convention-breaking changes
Notes
Auto-update is enabled, so this is confirmed on the latest available version (2.1.119)
Symptoms began after the April 20 postmortem fix (v2.1.116+), suggesting the fix is incomplete or introduced a new regression
Not using --resume for affected sessions
Follow-up on v2.1.119 — symptoms persist after April 20 fix
Environment:
Symptoms (observed April 25–26, 2026 — post-fix):
Notable: symptoms began after v2.1.116 postmortem fix. This is either an incomplete fix or a new regression introduced in 2.1.116–2.1.119.
Will run
/feedbackand append the ID here.Feedback ID:
8aa8e5b8-449f-4594-baff-e00bc92dd6f7Session: 46dfd054 (April 25–26, 2026, 178 turns)
Updated Feedback ID (submitted from the affected session):
24e68906-d711-47fc-9d59-4313ec53d75fSession ID:
46dfd054-7607-457b-842f-fb6a7ed03a90Previous feedback ID
8aa8e5b8-449f-4594-baff-e00bc92dd6f7may have been from a different session — please use this one.This issue has been automatically locked since it was closed and has not had any activity for 7 days. If you're experiencing a similar issue, please file a new issue and reference this one if it's relevant.