GPT 6 Astra

What is GPT 6 Astra?#

GPT 6 Astra is OpenAI's top model, released to trusted partners on September 3, 2026 and to everyone the day after. It runs on Unifically as openai/gpt-6-astra. It takes text and image input, returns text, and works across a context window of about 1 million tokens with knowledge up to April 30, 2026. OpenAI built it for computer use, coding, browsing, science, and long professional tasks, and it is the first OpenAI model to meet the company's Critical threshold for cybersecurity, which shapes what it will and will not do. Reasoning effort is tunable from low up to max, and every number below comes from the max setting.

Key features of GPT 6 Astra#

First on Terminal-Bench 4.0

Astra tops the independent Terminal-Bench 4.0 board at 58.2%, ahead of Claude Fable 5.1 at 57.9% and Claude Opus 5 at 51.8%, and it got there on 1.5B tokens where Fable 5.1 spent 2.7B. The benchmark tests agents on real terminal work: software engineering, system setup, and data analysis.

The best computer-use model published

On Agents' Last Exam, which runs professional tasks inside real software, Astra scores 59.3% against 55.5% for Claude Opus 5 and 53.6% for GPT 5.6 Sol, using about 65% fewer output tokens than Opus 5. On OSWorld 2.0 it reaches 72.6% in roughly 40 minutes per task, where Sol needed 75 minutes for 65.7%.

Science and math at the ceiling

Astra scores 96.0% on GPQA Diamond and 97.6% on FrontierMath Tier 4, up from 83.0% for Sol and 87.8% for Fable 5.1. On Terminal-Bench Science 0.1, which has agents run simulations and fit models from the command line, it reaches 64.6% against 52.6% for Fable 5.1.

Professional work that follows your template

AutomationBench, a run of multi-step business workflows across apps, goes to 41.4% against 31.4% for Fable 5.1 and 18.1% for Sol. Astra is trained to pull only the context a document needs and to keep to an existing slide or spreadsheet template, so outputs need less cleanup before they go out.

Best for#

Use cases#

Point Astra at a browser or desktop and let it work: fill forms, update records, run a research pass, and draft the summary in your document editor, then check the result on screen. Build a coding agent that plans a change across a repo, runs the tests in a terminal, and keeps going until they pass. Astra also lets Codex keep notes across context windows instead of squashing a long session into one summary. For back-office work, chain it through multi-app workflows that previously needed a person in the loop, and hand it a slide or spreadsheet template to fill. Research groups get a model that runs simulations, fits models, and reads sequencing output from the command line, and engineering teams can have it lay out a PCB or turn multi-view renders into CAD code.

Limitations#

Astra runs behind live misuse classifiers. It refuses advanced offensive security tasks such as building proof-of-concept exploits, and when a task is flagged it stops in the API instead of pausing for review. OpenAI plans to widen access for approved defenders through its Daybreak program, but at launch the API version is the restricted one.

OpenAI's own tests found Astra's written reasoning harder to monitor than Sol's, because it can solve simpler problems in fewer written steps. That matters if you rely on reasoning traces to audit an agent.

It is slow at max effort. Artificial Analysis measures about 62 output tokens per second and a long wait before the first answer token on hard prompts. Its independent Intelligence Index score is 55, second of 202 models, so the top setting is worth the wait only when the task needs it.

Astra is not ahead everywhere. Claude models keep the lead on Humanity's Last Exam with tools, 65.0% for Fable 5.1 against 57.2%, and on the Artificial Analysis Coding Agent Index v1.4, where Opus 5 scores 68.1 against 67.0. Output is text only.

GPT 6 Astra vs GPT 5.6 Sol#

Astra is the bigger jump on agent work. In OpenAI's runs Terminal-Bench 4.0 rises from 37.3% to 57.9%, Agents' Last Exam from 53.6% to 59.3%, AutomationBench from 18.1% to 41.4%, and SRE-Bench reverse engineering from 55.9% to 88.0%. ARC-AGI-3 goes from 7.8% to 99.9%, and the ARC Prize team says Astra matched its human action-efficiency baseline on 96% of levels. Academic scores move less: GPQA Diamond 94.6% to 96.0%, ARC-AGI-2 92.5% to 95.0%. Astra is also the more careful model, with a 2.4% misaligned-outcome rate on OpenAI's computer-use stress test against 22.0% for Sol, and zero attempts to route around a Codex auto-review denial. If Sol has been your default for agents, Astra raises the ceiling; Sol stays a fine choice for chat and routine coding.

When to use GPT 6 Astra#

Use Astra when the task is a long, multi-step job in real software: a computer-use agent, a repo-wide coding run, a business workflow across several apps, or a research loop in a terminal. Raise the effort as the problem gets harder. For quick replies, drafting, and everyday code, GPT 5.6 Sol or Terra answer faster for less, and Luna covers high-volume calls.

API examples#

Call GPT 6 Astra from any language by POSTing to /v1/chat/completions, the OpenAI-compatible endpoint shared by every language model on the platform. Full parameter docs live at docs.unifically.com/models/llm/openai/gpt-6-astra.

curl -X POST https://api.unifically.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "openai/gpt-6-astra",
    "messages": [
      { "role": "user", "content": "Plan and apply a migration that splits this orders table, then run the test suite." }
    ]
  }'

The response comes back synchronously with the completion. Set "stream": true to receive tokens as they generate, and "reasoning_effort": "low" when you want a faster, cheaper answer.