GPT-5.4 nano - A lightweight, fast AI model from OpenAI.

What is GPT-5.4 nano?

GPT-5.4 nano is the lightest and fastest version of GPT-5.4 released by OpenAI, designed for simple, high-throughput tasks with extremely high speed and cost requirements. The model performs exceptionally well in classification, data extraction, ranking, and lightweight sub-agent tasks, with an input cost of only $0.20/million tokens and an output cost of $1.25/million tokens, approximately 1/12th the cost of GPT-5.4. Currently, it is only available through an API.

Main functions of GPT-5.4 nano

• Classification tasksIt enables rapid classification and tagging of text, images, and other content, suitable for scenarios such as content moderation, sentiment analysis, and topic categorization.
• Data extractionThe model can accurately extract structured data and key information from unstructured documents, web pages, or tables, and supports entity recognition and field parsing.
• Sort and filterIt supports prioritizing, relevance scoring, and intelligent filtering of massive amounts of content, enabling efficient information retrieval and recommendation.
• Lightweight sub-agentAs a sub-agent, it performs simple auxiliary tasks, handling low-complexity sub-tasks such as searching, verifying, and formatting.
• Real-time response serviceIt provides extremely low-latency AI capabilities to support high-concurrency scenarios such as chatbots, customer service systems, and real-time recommendations.

Key information and usage requirements of GPT-5.4 nano

• positionOpenAI's lightest and fastest version of GPT-5.4, designed for simple, high-throughput tasks.
• speedFastest and lowest latency in the GPT-5.4 series.
• performanceIt performs excellently in lightweight tasks such as classification, data extraction, and sorting, but its capabilities for complex tasks are limited.
• ContextStandard Context Window
• PricingInput $0.20/million tokens, output $1.25/million tokens (approximately 1/12 of GPT-5.4)
• Access ChannelAPI only

The core advantages of GPT-5.4 nano

• Extreme speedAs the fastest model in the GPT-5.4 series, the GPT-5.4 nano has the lowest response latency, providing instant feedback for real-time interactive scenarios.
• Lowest costWith an input price of only $0.20 per million tokens and an output price of $1.25 per million tokens, which is about 1/12 of GPT-5.4, it is suitable for large-scale deployments with limited budgets.
• High concurrency supportThe model is specifically designed with an architecture optimized for high-throughput scenarios, capable of handling a massive number of simple requests simultaneously without sacrificing response speed.
• Lightweight and efficientIt performs well in simple tasks such as classification, data extraction, and sorting, and completes standardization work with extremely low computational cost.
• Flexible combinationIt can be used in conjunction with GPT-5.4 or GPT-5.4 mini to process simple sub-tasks as an edge sub-agent, thereby optimizing the overall system cost.
• Rapid deploymentThe model has the smallest size and the fastest startup speed, making it suitable for resource-constrained edge computing environments and business scenarios that require rapid expansion.

How to use GPT-5.4 nano

• API callsIt can be directly called through the OpenAI API, supporting text and image input, basic tool usage and function calls, but requires API access permissions and corresponding quotas.

Application scenarios of GPT-5.4 nano

• Content classification scenariosIt can quickly tag, classify, and perform sentiment analysis on massive amounts of text and images, and is suitable for social media content moderation, news topic classification, and user comment filtering.
• Data extraction scenariosIt can extract structured data in batches from unstructured documents, web pages, and tables, and is suitable for resume parsing, invoice information extraction, and key field identification in contracts.
• Sorting and filtering scenariosIt performs relevance scoring and priority ranking on search results, recommended content, and candidate lists, and is applicable to e-commerce product recommendations, job resume screening, and personalized information feeds.
• Light quantum intelligent agent scenarioAs a sub-agent, it performs edge tasks such as verification, formatting, and simple queries, and works with GPT-5.4/mini to build a low-cost multi-agent system.