Know GPT Astra. Then put it to work — right here.

What's actually confirmed about GPT-6 Astra

OpenAI's 3 September 2026 announcement describes GPT-6 Astra rolling out to eligible ChatGPT plans, the OpenAI API, Azure, and AWS Bedrock, under the documented API identifier gpt-6-astra. The confirmed specification sheet is short but concrete: a 1.05M-token context window with a 128,000-token maximum output, a knowledge cutoff of 30 April 2026, and configurable reasoning effort from low through max — there is no none level, so every request carries some reasoning cost. The documented Responses API surface includes streaming, function calling, structured outputs, web and file search, code interpreter, hosted shell, computer use, MCP, and tool search. Astra does not support fine-tuning, and audio and video are not listed as supported modalities on OpenAI's own model page.

What isn't disclosed matters just as much for planning: parameter count, dense versus mixture-of-experts topology, layer count, training-token composition, and exact reward-model design are all undisclosed. Treat GPT-6 Astra as a tool-using reasoning system defined by its interfaces and limits, not by a guessed architecture diagram. The full GPT Astra status brief keeps every claim tagged as confirmed, provider-stated, or editorial inference, so you can see exactly which category a given fact falls into before relying on it.

GPT-6 Astra pricing, without the spreadsheet

OpenAI's published GPT Astra API pricing is $10 per million standard input tokens and $50 per million output tokens. Cached input runs $1 per million tokens, a fresh cache write costs 1.25× standard input at $12.50 per million, and eligible Batch or Flex workloads receive a 50% discount. Fast-mode processing costs 2× the applicable rate, and any request whose input exceeds 272,000 tokens is billed at the long-context rate — 2× input and cache pricing, 1.5× output pricing — for the whole request, not just the overage. None of this includes tool-call fees, cloud-provider markup, or taxes. The GPT Astra cost calculator turns those rates into a monthly estimate once you enter your expected request volume, average token counts, and cache-hit rate.

How GPT Astra compares to the frontier

GPT-6 Astra sits at the top of OpenAI's own lineup: $10/$50 per million tokens versus GPT-5.6 Sol at $4/$20, Terra at $2/$12, and Luna at $0.20/$1.20. Outside OpenAI, the comparable frontier tier includes Anthropic's Claude Fable and Mythos lines, Google's Gemini 3.8 Flash, Moonshot's open-weight Kimi K3, and DeepSeek's V4 Pro and V4.1 Flash models, each with a different mix of context length, tool maturity, safety-framework transparency, data-residency guarantees, and price per completed task. There is no universal "best" model in that list — the detailed GPT Astra comparison page walks through a task-to-model decision matrix and a bake-off method so the choice is based on your own evaluation, not a single leaderboard number.

Turning Astra into a real workflow

The most reliable way to use GPT-6 Astra in production is brief → plan → script → verify → repair: write an executable brief with explicit constraints, ask Astra for a plan before any code, request one bounded change at a time, run it somewhere disposable, and verify the result yourself rather than trusting the model's own explanation. The GPT Astra for Blender workflow guide applies exactly this pattern to 3D scenes — brief, reviewed bpy script, execution in a throwaway .blend file, and evidence-based debugging. For a shipped application, the deploying GPT Astra guide covers the other half of the job: keeping API keys server-side, enforcing a model allowlist and per-user spend limits, validating every tool call against a schema, and gating any launch behind an evaluation threshold and a kill switch.