Detailed Analysis
A Reddit user posting to r/Anthropic has voiced frustration over significant performance degradation in Claude Opus, Anthropic's most capable and computationally intensive model tier, reporting that tasks now take roughly an hour to complete that previously finished in a fraction of that time. The complaint, while brief, reflects a pattern of user dissatisfaction with response latency that has surfaced repeatedly across Anthropic's community forums and developer channels. The user's note that the model "used to be faster for same effort" suggests the slowdown is a regression rather than an inherent characteristic of the model, pointing toward infrastructure, demand, or throttling changes on Anthropic's backend.
Opus models have historically occupied the premium end of Anthropic's Claude lineup, designed for complex, multi-step reasoning tasks that justify higher computational cost and longer wait times relative to lighter models like Haiku or Sonnet. However, there is a practical threshold beyond which latency undermines utility entirely — particularly for developers and power users who rely on the model for iterative coding, long-document analysis, or agentic workflows. When a model takes an hour per task, users effectively cannot complete their allotted usage within a session, as the complaint implies, rendering their subscription or API credits functionally inaccessible.
The issue likely reflects demand-side pressure on Anthropic's inference infrastructure. As Anthropic has expanded its user base and enterprise deployments significantly through 2025 and into 2026, compute resources for flagship models face mounting contention. Providers across the AI industry, including OpenAI and Google, have faced similar complaints about degraded throughput during peak periods, particularly for their largest models. Anthropic's response has typically been to encourage users toward faster, mid-tier models like Claude Sonnet, which now carries much of the capability formerly exclusive to Opus at considerably lower latency.
Broader trends in AI deployment suggest this tension between capability and speed is unlikely to resolve quickly. The most powerful frontier models remain extraordinarily resource-intensive, and scaling inference infrastructure to meet demand requires capital investment that lags behind user growth. Anthropic has been investing heavily in its cloud partnerships, including with AWS and Google Cloud, but raw infrastructure buildout takes time. User frustration like this post represents a real retention and satisfaction risk, particularly for paying subscribers who selected Opus specifically for its advanced capabilities and now find those capabilities effectively throttled by wait times that break practical workflows.
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