GPT

The trade-off between model age and reasoning effort Many developers assume that newer iterations of GPT models always deliver superior results or that older models provide a cheaper, token-saving alternative for trivial tasks. To test this theory, I pitted GPT 5.5 against its predecessors, GPT 5.4 and GPT 5.3, in a controlled Laravel API build. The experiment aimed to determine if the newer 5.5 medium model could outperform older models set to higher reasoning levels when tasked with adhering to the strict JSON:API standard. Performance metrics reveal the cost of intelligence The results immediately debunked the "older is cheaper" myth. While GPT 5.5 medium was the fastest, finishing in just two minutes and consuming only 2% of the usage limit, it failed the automated tests. In contrast, the GPT 5.4 X-High model took seven minutes and swallowed 5% of the limit. The GPT 5.3 Codex model fell in the middle, requiring four minutes and 3% usage. Crucially, the "High" and "X-High" reasoning settings—regardless of the model version—produced code that actually worked. Intelligence level, not model version, is the primary driver of both cost and quality. Analysis of code quality and standards adherence The code comparison highlighted a significant architectural failure in the 5.5 medium output. It dumped the entire API logic into the routes file—a major red flag for maintainability—and failed to implement correct pagination parameters. Conversely, both GPT 5.4 and GPT 5.3 correctly utilized the `page[number]` and `page[size]` query parameters required by the JSON:API specification. Surprisingly, none of the models leveraged the latest `JsonApiResource` available in Laravel 12, suggesting a slight lag in their training data or documentation retrieval despite active context querying. Final verdict on model selection If you require precision and adherence to specific architectural standards, opting for high-reasoning models is non-negotiable. The 5.5 medium model is a budget-friendly option for rapid prototyping, but it lacks the nuance to handle strict specifications like JSON:API without manual intervention. For production-grade code where "one-shotting" is the goal, the extra cost of GPT 5.4 X-High is a justified investment in accuracy.