Yes, GPT-5.4 is accessible via an API. OpenAI officially released GPT-5.4 on March 5, 2026, making it available through their API, in ChatGPT for Plus, Team, and Pro users, and in Codex. This model represents an advancement in reasoning, programming, and professional workflows, designed for complex tasks and agentic systems. Developers can integrate GPT-5.4 into their applications by creating an OpenAI account, setting up billing, generating an API key, and using the OpenAI SDK to make requests to the gpt-5.4 model. The API supports various capabilities, including custom tools, web search, file search, image generation, code interpretation, and built-in computer use.
Accessing the GPT-5.4 API requires a paid account, as there is no official free tier. Pricing is structured on a pay-as-you-go basis, typically costing $2.50 per million input tokens and $15 per million output tokens, with a Pro version available at higher rates for more complex tasks. OpenAI provides official SDKs for Python and Node.js, and developers can use their existing openai Python SDK by simply setting the model="gpt-5.4" parameter. The API maintains the same format for response, tool calling syntax, and streaming API as previous GPT models, ensuring compatibility.
Key features of GPT-5.4 through the API include an experimental 1 million token context window, which allows for processing large amounts of information in a single request, and improved tool search mechanisms that reduce token consumption in agent workflows. This context window enables analysis of entire codebases or extensive document collections. Furthermore, GPT-5.4 introduces native computer use capabilities, allowing the AI to interact directly with software for task completion and verification. These enhancements, along with a focus on reducing factual errors by 33% compared to GPT-5.2, make GPT-5.4 a powerful tool for developers aiming to build advanced AI applications, especially those involving agentic systems or vector databases like Milvus for efficient similarity searches over large datasets of vector embeddings.