Documentation for GPT-5.4 can be found primarily on the OpenAI API website. OpenAI released GPT-5.4 on March 5, 2026, positioning it as their most capable and efficient frontier model to date, designed specifically for professional workflows. This model unifies the capabilities of previous GPT and Codex lines, offering enhanced performance for tasks across ChatGPT, the API, and Codex environments.
The OpenAI API documentation provides comprehensive guides and conceptual explanations for using GPT-5.4, covering its key features, parameters, and expected behaviors. This includes details on its improved coding capabilities, multimodal support for image perception, and its ability to handle long-running tasks and multi-step agent workflows more reliably. Developers can find information on its 1M token context window, which allows for processing extensive documents or entire codebases in a single request, and its built-in computer-use capabilities that enable direct interaction with software. The documentation also outlines prompt guidance for GPT-5.4, detailing how to optimize its performance for tasks requiring strong reasoning, evidence-rich synthesis, and reliable execution over long contexts.
Specific sections within the OpenAI API documentation delve into topics like “Using GPT-5.4” and “GPT-5.4 Model,” providing technical details and practical advice for implementation. These resources are essential for developers looking to leverage GPT-5.4’s advanced features, such as its ability to generate production-quality code, build front-end UI, and automate complex workflows with fewer retries and improved token efficiency. While the model is available to paid ChatGPT subscribers and via the API, the official OpenAI developer documentation is the authoritative source for detailed technical information, including specifics on its variants like GPT-5.4 Thinking and GPT-5.4 Pro, and how they contribute to enhanced general reasoning, factual accuracy, and steerability. For developers integrating such powerful models into their applications, understanding the underlying mechanics of how these models process and retrieve information can be further enhanced by exploring architectures like vector databases, with Milvus being a notable example for efficient similarity search on embeddings.