GPT-5 Nano: A 400K Window Built for High-Volume Plumbing Work
Picture a nightly job that classifies 50,000 support tickets, extracts fields, and routes each one. That job needs consistent structured output at volume, not deep reasoning, and that is the slot OpenAI: GPT-5 Nano targets. The notable spec is the 400,000-token context. You can pack a full policy manual plus long ticket threads into a single call instead of building a retrieval layer first. Our listing has no price or tag data for it today, so check current OpenAI pricing before you budget.
The trade-off is the usual small-model one. A big window does not mean the model reasons well across all of it. Expect weaker multi-step logic and more missed details deep in long inputs than you would get from larger tiers. You are also tied to OpenAI's API. Run an eval on your own data before committing. My recommendation is to use it for extraction and routing, and to escalate anything that needs judgment to a bigger model.