A growing chorus of developers and AI power users have voiced concerns across social media about perceived drops in the performance of Anthropic’s Claude Opus 4.6 and Claude Code models. These users argue that the models have become less reliable for complex coding tasks, exhibiting issues like premature task abandonment, increased hallucinations, and inefficient token use compared to weeks prior. Discontent spread rapidly on platforms like GitHub, X, and Reddit, with some branding this phenomenon as “AI shrinkflation” where consumers pay the same but get less. While Anthropic denies intentional degradation and stresses adjustments are linked to user interface changes and default settings aimed at balancing token consumption, some users remain unconvinced. The debate intensified following benchmark comparisons which suggested performance drops, though external analysts flagged inconsistencies in the benchmark data, attributing some results to differing testing scopes rather than actual regression. Additionally, recent policy changes on usage limits and cache behaviors have sparked concerns about cost and efficiency impacts in longer AI sessions. Anthropic representatives have publicly refuted claims of deliberate model downgrades, asserting instead that the updates improve overall user experience and efficiency without compromising core capabilities. This controversy highlights a broader trust issue, where changes in interface and product settings may feel like performance drops to heavy users even if the underlying model remains intact. This discourse unfolds at a critical juncture as competitors like OpenAI escalate efforts to enhance their AI coding tools, intensifying the stakes in this high-demand area.
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