GPT-6 Astra

What GPT 6 is

GPT-6 Astra is the launch model in OpenAI’s GPT-6 family, announced on September 3, 2026. It is designed for connected tasks such as researching a question, working with files, writing software, and checking the result with tools. This independent fieldguide brings together official access information, API pricing, and practical resources you can copy or download.

In ChatGPT, Astra is offered as GPT-6 Pro for eligible Pro, Business and Enterprise plans. Plus includes Astra in Work and Codex. The API model identifier is gpt-6-astra, with separate billing and access requirements. Start with the product you intend to use.

For the launch date and staged rollout, see OpenAI’s GPT-6 Astra: A new generation of intelligence; for the ChatGPT-facing name, see OpenAI Help’s GPT-5.6 and GPT-6 Pro in ChatGPT; for the developer identifier, see the OpenAI API GPT-6 Astra Model.

Find your GPT 6 starting point

Your task	Start here	What you will find
Check the announcement	Release timeline	Confirmed date and staged rollout
Find the model in ChatGPT	ChatGPT access	Product names, plans and workspace checks
Get access in your product	How to access GPT-6	Steps for Chat, Work, Codex and API, plus missing-model checks
Estimate the cost	Astra pricing guide	API rates, cache and long-context rules, and worked cost estimates
Build an integration	API guide	Exact model ID, pricing and request setup
Get a useful first result	Prompt library	Fifteen complete prompts with filled examples
Inspect a finished workflow	Practical examples	Documents, a browser QA exercise and a playable game

Resources you can actually use

The library covers everyday planning, office work, research, coding and creative work. Each prompt has a complete instruction, an output format and checks. Copy prompt copies the reusable version. Copy example input copies the same instruction with the example materials filled in. The original response and any correction are shown separately, so copying an input never mixes in the answer.

For a quick office task, try turning meeting notes into an action list. The example deliberately mixes decisions, proposals, deadlines and missing owners. For a coding task, inspect a small review containing a percentage-calculation error and an array-mutation bug. These examples make omissions and assumptions easier to spot than an impressive-looking answer without its input.

The case collection goes further: compare a source document with its formatted DOCX and PDF, inspect changes between two synthetic records, follow a reproducible frontend fix, or play Catch the Stars. Downloadable files let you examine the actual artifact beyond a screenshot.

How our GPT 6 examples were run

This resource edition uses GPT-6 Astra at medium effort in Codex. Inputs use synthetic workshop materials, small code samples and public sources. The current-editor comparison uses official VS Code and Zed documentation. Each record keeps the complete input, first response, independent checks and any later revision.

These runs describe a particular environment and input. They do not establish ChatGPT-interface behavior or paid API results. Official customer stories remain attributed to the organization that published them. Where a run has a concrete limitation, the record names it beside the result.

A useful first task

Choose an input small enough to inspect: one meeting, a short document or a tiny diff. Decide what a correct answer must preserve before running the prompt. For a meeting, that may mean every decision has a source line and missing owners remain missing. For a document, it may mean the negative sign, hyperlink destinations and footnote survive an export.

Then perform the cheapest independent check. Recalculate a weighted score, run the reproduction, open the download, or compare the source IDs. If the result needs a correction, change one part of the instruction or input and keep the original response. You will learn which change helped instead of merely collecting a smoother answer.

Official facts and site results

The announcement, Help Center and model reference below are the sources for release, naming and developer facts. Our examples add concrete inputs, outputs and methods. The access information reflects the dated source check; the experimental records carry their own run dates. This site is maintained independently and is not affiliated with or endorsed by OpenAI.