Model Releases
OpenAI GPT-5.4 Series: The 1M Token Frontier Model
OpenAI unveils GPT-5.4 with a massive 1M token context window, native computer use, and enhanced reasoning capabilities for professional workflows.
March 6, 2026
Model ReleaseGPT-5.4 Series
Introduction
On March 6, 2026, OpenAI officially released the GPT-5.4 Series, marking a significant leap forward in the capabilities of large language models. This flagship release is designed specifically to meet the rigorous demands of professional and technical workflows, distinguishing itself from previous iterations through architectural efficiency and expanded context handling. The model introduces native computer use, allowing AI agents to interact with operating systems and applications directly, a feature that promises to revolutionize automation and developer productivity.
For developers and AI engineers, the GPT-5.4 Series represents a shift from text generation to action-oriented reasoning. By integrating a reworked tool-calling system and supporting reasoning effort with four distinct levels, OpenAI aims to provide more granular control over model behavior. This release sets new records on professional benchmarks, challenging competitors like Anthropic and Google to match its performance in complex navigation and reasoning tasks.
- Released on 2026-03-06
- Flagship model for professional work
- Introduces native computer use capabilities
Key Features & Architecture
The architecture of GPT-5.4 is built around efficiency and scale, featuring a massive 1-million-token context window. This allows the model to ingest entire codebases, lengthy legal documents, or hours of video transcripts in a single pass without losing coherence. OpenAI has optimized the model to handle this scale while maintaining low latency, ensuring that developers can work with large datasets without the traditional token truncation issues seen in earlier generations.
The series is available in three distinct variants to cater to different latency and cost requirements: Standard, Mini, and Nano. A standout feature is the support for reasoning effort, which allows users to adjust the model's cognitive load through four specific effort levels. This flexibility enables developers to balance speed and accuracy dynamically, optimizing costs for production environments where reasoning depth varies by task.
- 1M token context window
- Standard, Mini, and Nano variants
- 4 reasoning effort levels
- 128K max output tokens
Performance & Benchmarks
In terms of raw performance, GPT-5.4 surpasses human benchmarks in desktop navigation and reasoning tests. The model has been evaluated on standard industry benchmarks including MMLU, HumanEval, and SWE-bench, showing consistent improvements over the previous GPT-5.2 iteration. Specifically, the model demonstrates a 15% increase in accuracy on complex coding tasks and a significant reduction in hallucinations during long-context RAG (Retrieval-Augmented Generation) workflows.