Anthropic’s Claude Opus 5.5 and OpenAI’s GPT-6 Sol launched on the same day — then GPT-6.1 Sol followed seven days later. That sequence isn’t an outlier. According to XDA Developers’ reporting, the average gap between major AI model releases from the top seven labs fell to eight days in early 2026, down from 38 days in 2023. One September count puts it at one new flagship every 17 days this year. The pace isn’t slowing. It’s accelerating. And users are hitting a wall: model fatigue is now a documented workflow condition, not just a meme. It shows up as unfinished test sessions, subscription stacking (Claude Pro plus ChatGPT Plus plus Gemini Advanced), and benchmark chart scrolling that yields no actionable insight. Users report older models feeling worse. Not just by comparison, but objectively. Anthropic traced a months-long dip in Claude Code performance to behind-the-scenes changes: lower default reasoning effort, cache bugs, and silent weight quantization. Those tweaks weren’t version bumps. They were live operational adjustments, invisible to users until output shifted. Benchmarks themselves are fracturing. GPT-6 Sol scored below GPT-5.6 Sol on two of OpenAI’s own reported benchmarks. Meanwhile, Alibaba released nine Qwen 3.5 variants in 16 days. Google dropped Gemma just one month after Qwen 3.5’s debut. Local models offer no refuge. Just another layer of churn. The coping strategy emerging isn’t faster adoption. It’s role-based discipline: assigning Opus 5.5 to deep research and brainstorming, Opus 4.7 to admin tasks, Gemini 3.1 Pro to studying, and Gemma E4B or Qwen 3.5 9B to local chat and coding. New models like Sonnet 5.5 and Haiku 5.5? Still unopened weeks after launch, not out of disinterest, but because testing them properly requires time most developers don’t have. The next release window isn’t a date on a calendar. It’s a decision point: whether to pause, assign, or finally unsubscribe.