Detailed Analysis
A Reddit user on r/Anthropic has reported a severe and persistent bug that has rendered their paid Claude account completely unusable for over five days. The error message—"This request exceeds Claude's context limit by about 5,560,808 tokens"—appears on every single interaction, including trivial one-word prompts, suggesting that some phantom context payload has become permanently attached to the user's account or session state on Anthropic's backend. Notably, the user reports this occurred spontaneously without any configuration changes, custom instructions, or unusual tool usage that might explain such a massive token overflow.
What makes this case particularly noteworthy is the thoroughness of the user's troubleshooting before going public. They disabled all tools, skills, and plugins; started fresh conversation threads; tested across multiple models, browsers, and the official mobile app; and cleared cookies and logged out of all devices. The fact that the error persisted across every surface and every reset attempt strongly suggests the problem lives server-side—likely in some corrupted account or project metadata, a stuck context cache, or a database state that improperly retains a stale token count from a previous session or file upload. This kind of bug is distinct from typical user-facing context-window limitations, which scale with actual conversation length; here, the reported overage (5.5 million tokens) vastly exceeds even Claude's largest published context windows, pointing to a data corruption or account-state bug rather than legitimate usage.
The more concerning element of this report is the support response, or lack thereof. The user states they opened a support ticket more than five days prior and received zero response, despite paying for a premium tier (implied to be the $100/month Max plan). For a service marketed toward professional and power users—many of whom rely on Claude for coding, research, or business-critical workflows—a multi-day total outage with no acknowledgment from support represents a significant reliability and trust failure. As AI assistants become embedded in daily professional workflows, the expectation for enterprise-grade uptime and responsive support grows accordingly, and stories like this highlight the gap between that expectation and current support infrastructure at fast-scaling AI companies.
This incident also reflects a broader pattern in the AI industry: rapid growth in user bases and feature complexity (tools, skills, plugins, MCP integrations, project-level context) increases the surface area for edge-case bugs involving session state, caching, and account-level data corruption. As Anthropic and competitors continue layering more persistent memory, tool-calling, and multi-turn context features onto their assistants, the risk of "ghost state" bugs—where stale or corrupted data silently breaks a user's entire experience—becomes more likely. Community-driven troubleshooting threads like this one often serve as an informal escalation path when official support channels lag, underscoring both the value and the limitations of user forums as a stopgap for enterprise-grade incident response in the fast-moving AI assistant market.
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