The Digital Press

Developers are increasingly selecting older AI models alongside newer ones based on task-specific strengths rather than simply adopting the latest version. Data from millions of coding interactions show that models like Sonnet 4.0 and 4.5 serve different roles, with specialization emerging in AI tooling.

Why it matters: Developers prioritize model fit for tasks, marking a shift from version upgrades to functional specialization.

The big picture: AI model usage is fragmenting, resembling how databases evolved into specialized types for different workloads.

The stakes: Deeper reasoning models like Sonnet 4.5 require more compute and introduce latency, impacting system performance.

Commenters say: Users appreciate diverse model strengths but note practical challenges with API inconsistencies, slow GPT-5 response times, and the cognitive overhead of switching models frequently.