Not a benchmark table. This puts pricing, context, interface fit, and key visibility into one decision card.
高性价比主力通用模型，适合规模化生产环境。
面向低成本高并发任务的入门位，适合分类、抽取和轻量子代理。
This is an internal OpenAI comparison, so the main question is tier, cost, context, and capability rather than provider switching.
On the listed price snapshot, GPT-5.4 Nano is cheaper on combined input and output, but real routing, discounts, and limits still matter.
When context is similar, compare output quality, API stability, limits, and actually callable models first.
Do not start with which model is absolutely stronger. Start with the boundary: cost, context, speed, quality, ecosystem, or supply stability.
If you already hold a key, the valuable check is provider identity, callable models, and whether balance, limits, or subscription status are visible.
Commercially, do not look at model names alone. Combine price, limits, region, upstream stability, and ongoing monitoring.
