GPT-5 Nano

GPT-5 Nano sits at the small end of the GPT-5 family from OpenAI, positioned as a cost-efficient variant that retains the multimodal reasoning core shared with its larger siblings. Within the GPT-5 lineup, it is the option designed for high-throughput, latency-sensitive workloads where the full GPT-5 stack would be overkill, making it a natural fit for embedding into Databricks pipelines that handle large volumes of routine text and image-understanding tasks. Because the family is part of OpenAI's reasoning-oriented generation, GPT-5 Nano continues the trajectory of pairing general language understanding with deliberate chain-of-thought style reasoning, which is reflected in its inclusion of reasoning, tool calling, and structured output capabilities on the platform.

In practice, GPT-5 Nano is best suited to teams that want the GPT-5 reasoning pattern applied to classification, extraction, routing, and short-form generation tasks running at scale inside Databricks notebooks, jobs, and serving endpoints. The very large context window combined with image input support allows it to digest long documents and screenshots alongside text, which is useful for document AI, log analysis, and visual question answering without escalating to a larger, more expensive model. Its low input and output pricing, plus a discounted cache read rate, makes it economical for batched, repetitive prompts, while the small footprint keeps latency low for interactive applications, offering a balanced trade-off for production deployments that prioritize volume and responsiveness over the deepest reasoning capacity of the full GPT-5 model.