GPT-5.4 Nano

OpenAI and Broadcom unveil LLM-optimized inference chip

OpenAI DevDay 2026 applications are now open! Our biggest developer event gets even bigger. 📍 San Francisco 📅 September 29 Apply by July 10: https://t.co/BJyK2EbKuu

Samsung Electronics brings ChatGPT and Codex to employees

As AI takes on longer, higher-stakes tasks, we want models to carry beneficial and safe behavior into new domains beyond their training—and maintain it under pressure. That’s the idea behind our new r

As AI takes on longer, higher-stakes tasks, we want models to carry beneficial and safe behavior into new domains beyond their training—and maintain it under pressure. That’s the idea behind our new research on training models to be broadly and persistently beneficial.

Improving health intelligence in ChatGPT

Introducing LifeSciBench, a benchmark for measuring and improving how well AI supports real-world life science research. Developed with 173 scientists from biotechnology and pharmaceutical research, L

Introducing LifeSciBench, a benchmark for measuring and improving how well AI supports real-world life science research. Developed with 173 scientists from biotechnology and pharmaceutical research, LifeSciBench includes 750 expert-authored tasks across seven biological research https://t.co/JDkKWcnL9F

Introducing LifeSciBench

Towards Retrieving Interaction Spaces for Agentic Search

Retrieval for search agents is still inherited from non-agentic information retrieval: a retriever ranks the corpus and the agent reads a small set of returned documents. Recent direct corpus interaction (DCI) work shows that agents can instead interact with the raw corpus through shell tools such as grep and file reads. But unbounded interaction does not scale: every broad shell command is a scan over the whole corpus, and latency degrades sharply as the corpus grows. We argue that the role of retrieval for agentic search is not just to select documents that fit in the LLM context window, but to construct an interaction space: a bounded subset of the corpus the agent can explore with associated tools. Two design consequences follow. The space needs a boundary supplied by retrieval, and the objects within it should be processed for interaction. As a proof of concept, we propose RISE (Retrieving Interaction SpacE): we use BM25 to construct the interaction space; meanwhile, its documents are processed during indexing for shell-style navigation. On BrowseComp-Plus, RISE matches the pure-shell DCI baseline at 78% accuracy with gpt-5.4-mini at roughly one quarter of the per-query cost. At 1M documents, RISE-BM25 reaches 81% on gpt-5.4-mini, whereas DCI on gpt-5.4-nano degrades to 60% with 33 of 100 wall-clock failures.

Models | OpenAI API

Models | OpenAI API Home API Docs Guides and concepts for the OpenAI API API reference Endpoints, parameters, and responses Codex Docs Guides, concepts, and product docs for Codex Use cases Example workflows and tasks teams hand to Codex ChatGPT Apps SDK Build apps to extend ChatGPT Workspace Agents Trigger published ChatGPT workspace agents Commerce Build commerce flows in ChatGPT Ads Publish and measure ads in ChatGPT Resources Showcase Demo apps to get inspired Blog Learni

Introducing GPT‑5 for developers

Introducing GPT‑5 for developers | OpenAI Skip to main content Research Products Business Developers Company Foundation (opens in a new window) Log in Try ChatGPT (opens in a new window) Research Products Business Developers Company Foundation (opens in a new window) Try ChatGPT (opens in a new window) Login OpenAI August 7, 2025 Product Introducing GPT‑5 for developers The best model for coding and agentic tasks. Loading… Share Introduction Introduction Coding Frontend engin