What is GPT-5.4 nano
GPT-5.4 nano is the lightest and fastest GPT-5.4 version released by OpenAI, designed specifically for simple high-throughput tasks with extremely high demands on speed and cost. The model performs excellently in classification, data extraction, ranking, and lightweight sub-agent tasks. Input pricing is only $0.20 per million tokens, output $1.25 per million tokens, about 1/12 that of GPT-5.4, and it is currently available only via API.
Key features of GPT-5.4 nano
- Classification tasks: Quickly classify and label text, images, and other content, suitable for content moderation, sentiment analysis, topic categorization, and similar scenarios.
- Data extraction: The model can accurately extract structured data and key information from unstructured documents, web pages, or tables, supporting entity recognition and field parsing.
- Ranking and filtering: Supports prioritizing massive content, relevance scoring, and intelligent filtering to enable efficient information retrieval and recommendations.
- Lightweight sub-agents: Acts as a sub-agent to perform simple auxiliary tasks, handling low-complexity subtasks such as search, verification, and formatting.
- Real-time response services: Provides extremely low-latency AI capability support for high-concurrency scenarios such as chatbots, customer service systems, and real-time recommendations.
Key information and usage requirements for GPT-5.4 nano
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Positioning: OpenAI's lightest and fastest GPT-5.4 version, designed specifically for simple high-throughput tasks
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Speed: Fastest in the GPT-5.4 series, with the lowest latency
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Performance: Performs excellently in lightweight tasks such as classification, data extraction, and ranking, with limited capability for complex tasks
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Context: Standard context window
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Pricing: Input $0.20 per million tokens, output $1.25 per million tokens (about 1/12 that of GPT-5.4)
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Access channel: Available only via API
Core advantages of GPT-5.4 nano
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Extreme speed: As the fastest model in the GPT-5.4 series, GPT-5.4 nano has the lowest response latency, providing instant feedback for real-time interactive scenarios.
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Lowest cost: Input pricing is only $0.20 per million tokens, output pricing $1.25 per million tokens, about 1/12 that of GPT-5.4, suitable for large-scale deployments with limited budgets.
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High-concurrency support: The model's architecture is optimized for high-throughput scenarios, capable of handling massive numbers of simple requests simultaneously without sacrificing response speed.
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Lightweight and efficient: Performs excellently in simple tasks such as classification, data extraction, and ranking, completing standardized work at extremely low computational cost.
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Flexible combination: Can be used together with GPT-5.4 or GPT-5.4 mini, acting as an edge sub-agent to handle simple subtasks and achieve overall system cost optimization.
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Fast deployment: The model has the smallest size and fast startup speed, suitable for resource-constrained edge computing environments and business scenarios requiring rapid scaling.
How to use GPT-5.4 nano
- API calls: Called directly through the OpenAI API, it supports text and image inputs, basic tool use, and function calling, and requires API access permissions and the corresponding quota.
Application scenarios of GPT-5.4 nano
- Content classification scenario: Quickly label and classify massive amounts of text and images and perform sentiment analysis, suitable for social media content moderation, news topic categorization, and user comment filtering.
- Data extraction scenario: Extract structured data in batches from unstructured documents, web pages, and tables, suitable for resume parsing, invoice information capture, and identification of key contract fields.
- Ranking and filtering scenario: Score relevance and prioritize search results, recommended content, and candidate lists, suitable for e-commerce product recommendations, recruitment resume screening, and information feed personalization.
- Lightweight sub-agent scenario: Serve as a sub-agent to perform edge tasks such as verification, formatting, and simple queries, working with GPT-5.4/mini to build low-cost multi-agent systems.