GPT-6 Astra is OpenAI's new flagship model for complex end-to-end work. OpenAI's official model documentation lists it as gpt-6-astra, with text and image input, text output, a 1.05-million-token context window, and support for tools including web search, computer use, hosted shell, code interpreter, MCP, and apply patch.
Rollout note: OpenAI says Astra is rolling out first to enterprises in its Trusted Access Program. API and ChatGPT Plus, Pro, Business, and Enterprise access are described as coming in the following days. Availability can therefore differ by account when you read this guide. Check the official GPT-6 Astra model page before planning a production migration.
In this guide
Verified GPT-6 Astra facts
| Specification | Official value |
|---|---|
| Product name | GPT-6 Astra |
| Model ID | gpt-6-astra |
| Input | Text and images |
| Output | Text |
| Context window | 1,050,000 tokens |
| Maximum input | 922,000 tokens |
| Maximum output | 128,000 tokens |
| Knowledge cutoff | April 30, 2026 |
| Reasoning effort | low, medium, high, xhigh, max |
| Fine-tuning | Not supported |
Source checked September 4, 2026 against OpenAI's official developer documentation. Specifications and access can change.
What is GPT-6 Astra?
OpenAI describes GPT-6 Astra as its most capable model for the hardest end-to-end work. Its documented use cases include complex reasoning, coding, computer use, research, and document creation. The important distinction is that Astra is not Google's Project Astra: Google's similarly named project is a separate DeepMind research effort around a real-time multimodal assistant.
Astra is designed for workflows that combine reasoning with actions. Instead of asking a model for one isolated answer, a developer can give it a larger objective, provide files or images, and let it use supported tools under explicit application controls. That makes evaluation of tool behavior and failure recovery as important as evaluating prose quality.
A 1.05M-token context window—what it means
The documented context window is 1,050,000 tokens, with up to 922,000 input tokens and 128,000 output tokens. This creates room for large repositories, document collections, long research trails, and multi-step agent state. It does not mean every task should send a million tokens.
OpenAI applies higher rates when prompts exceed 272,000 input tokens: input and cache rates become 2×, while output is priced at 1.5× for the full request. For production systems, retrieval and compaction remain important. Smaller, relevant context is normally cheaper and easier to evaluate than an unfiltered data dump.
Supported tools and workflows
The official documentation lists support through the Responses API for web search, file search, image generation, code interpreter, hosted shell, apply patch, skills, computer use, MCP, and tool search. Streaming, structured outputs, function calling, prompt caching, and image input are also supported.
That combination makes Astra relevant to repository-scale engineering, research synthesis, document production, and computer-use agents. Applications still need permission boundaries, confirmations for consequential actions, audit logs, timeouts, and independent checks. Tool availability is not evidence that every autonomous run will be correct.
GPT-6 Astra API pricing
| Token type | Price per 1M tokens |
|---|---|
| Input | $10.00 |
| Cached input | $1.00 |
| Cache writes | $12.50 |
| Output | $50.00 |
OpenAI says Batch and Flex are priced at 50% of Standard rates, while Fast mode is 2× the applicable rates. Tool-specific charges may also apply. Confirm current prices on OpenAI's official pricing page before estimating production cost.
How to evaluate GPT-6 Astra before migrating
- Confirm access. Do not assume the model is enabled for your account during rollout.
- Create a representative evaluation set. Include your real coding, research, document, and tool-use tasks—not generic benchmark prompts.
- Start with medium reasoning effort. Compare quality, latency, and token usage before raising it to high, xhigh, or max.
- Measure end-to-end cost. Include retries, long-context multipliers, cache writes, output tokens, and paid tool calls.
- Test tool failures. Simulate missing files, denied permissions, stale search results, command failures, and partial completion.
- Keep human approval for consequential actions. Code deployment, purchasing, account changes, and external communication should not be silently automated.
- Roll out gradually. Shadow existing workflows first, then move a small percentage of production tasks after the model passes your evaluations.
Important limitations
- Astra accepts images but does not return native image, audio, or video output from the model endpoint.
- Realtime, Assistants, fine-tuning, embeddings, and legacy Completions are listed as unsupported.
- A large context window does not eliminate hallucinations, stale knowledge, prompt injection, or tool-use errors.
- The April 30, 2026 knowledge cutoff means current facts still require web search or supplied sources.
- Rollout availability and rate limits depend on account and usage tier.
Frequently asked questions
Is Astra the same as Google Project Astra?
No. GPT-6 Astra is an OpenAI model. Project Astra is a separate Google DeepMind research project for a real-time multimodal assistant.
What is the GPT-6 Astra model ID?
The official API model ID is gpt-6-astra.
Can GPT-6 Astra process images?
Yes. OpenAI lists text and image input, with text output.
Is GPT-6 Astra available to everyone?
Not necessarily yet. OpenAI documents an enterprise Trusted Access rollout first, followed by API and Plus, Pro, Business, and Enterprise access in the coming days.
Should I immediately replace my existing model?
No. Evaluate Astra on representative tasks, quality, latency, safety, and total cost. Migrate gradually only when it performs better for your workload.
Sources
- OpenAI: GPT-6 Astra model documentation
- OpenAI: current model catalog
- Google DeepMind: Project Astra—included to disambiguate the name.
