Do older models still matter when a new one launches?

Every time a new AI model comes out, it’s framed as a clear upgrade. But in practice, I’m not sure people switch that quickly.

If an older model already works well in your workflow, moving to a new one means re-testing prompts, output quality, latency, and cost. Sometimes the new model is better overall, but not for the specific task I actually care about.

Curious how others handle this:

• Do you switch to new models like Claude Opus 4.8 right away?
• Do you keep older models in your workflow for specific reasons?
• What usually makes a new model worth switching to?