OpenAI: GPT-5 Nano

For a task like classifying thousands of short inputs an hour, where each call must be cheap and fast, this is the GPT-5 variant that earns its keep. It is the smallest and fastest model in the family, tuned for developer tools, rapid interactions, and ultra-low-latency environments, and its standout figure is price: 0.05 dollars per million input tokens, described as cheaper than roughly 75 percent of comparable models. Even at that cost it keeps a 400,000-token context window bigger than most models listed here, image input, chain-of-thought reasoning, and tool calling, though its reasoning depth is limited compared with larger siblings and its knowledge cutoff is May 2024. A concrete use case is a real-time moderation or tagging service that must return structured labels on a high-volume stream without adding noticeable delay. Against alternatives such as Claude, Gemini, or Claude Sonnet 5, the differentiator is raw cost-per-token and latency rather than sophistication, and against the mini or full GPT-5 it deliberately sacrifices reasoning for throughput. The one drawback is no free tier. Ideal for developers building latency-sensitive, high-volume features who need adequate quality at the lowest price. Reach for it when speed and unit economics dominate; step up a tier the moment a task rewards genuine reasoning depth.