GPT-6 Astra Is Live on Smart AIPI — 75% Off, and the Real Story Is Token Efficiency

OpenAI announced GPT-6 Astra on September 3, 2026, calling it "the world's most intelligent and aligned model." It is available in the API as gpt-6-astra, and it is available on Smart AIPI right now at a quarter of OpenAI's price.

Launch posts are marketing. This one isn't — we sell access to the model, so the useful thing we can do is tell you exactly where it wins, where it loses, and what it will actually cost you. All of the numbers below come from OpenAI's published evaluation tables and from independent analysis of them.

The headline results

Astra is state of the art on computer use, browsing, software engineering, cybersecurity, science and professional work. The scores that matter most to developers:

A few of these deserve a sentence each. Terminal-Bench 4.0 is the closest thing we have to "can an agent operate a real machine and finish real work" — Astra's 57.9% against Sol's 37.3% is a generational jump, not a rounding error. AutomationBench (multistep business workflows across applications) more than doubles Sol. On OSWorld 2.0, Astra scores higher and finishes in about 40 minutes per task versus Sol's 75 — roughly 47% less time. And the alignment row is not a footnote: on an adversarial computer-use safety test, Astra produces misaligned outcomes 2.4% of the time against Sol's 22.0%. If you are letting an agent touch production, that number is worth more than a benchmark point.

Three caveats the launch charts don't show

We would rather you hear these from us than discover them yourself.

1. The 99.9% on ARC-AGI-3 is a harness result

ARC-AGI-3 evaluates a model by handing it a 64×64 grid of colored cells as a JSON object and letting it emit action tokens into a simulated environment — no screenshots, no video, no rules explained up front. The 99.9% figure was produced using OpenAI's own Responses API harness, with its own context management. Run the same model through the ARC Foundation's hosted harness with plain API calls, and Astra scores about 62.7%.

Both numbers are honest; they measure different systems. But it tells you something important about 2026: the harness is now a first-class part of the score. When you evaluate a model for your own stack, evaluate it inside your harness, not someone's press chart.

2. Composite indices disagree, loudly

OpenAI highlighted 14 benchmarks in its announcement. Exactly one of them overlaps with the Artificial Analysis Intelligence Index, which ranks Astra fifth at 61.2 — behind Claude Fable 5.1 (65.7), Claude Opus 5 (63.1) and Claude Fable 5 (62.1). On the Artificial Analysis Coding Agent Index v1.4, Astra is 67.0 against Opus 5's 68.1 and Fable 5's 67.2. And Astra loses Humanity's Last Exam with tools to Fable 5.1, 57.2% to 65.0%.

Aggregate indices are wrappers around benchmarks, and a wrapper can either surface signal or bury it. The honest read: Astra is clearly the leading model for agentic, terminal, and computer-use work, and it is not the undisputed leader on every measure of "intelligence." Pick the model that wins on the axis you actually ship on.

3. Cyber capability is gated, and the reasoning trace got shorter

Astra meets the Critical cybersecurity threshold under OpenAI's Preparedness Framework — 100% on ExploitBench, 42.4% on ExploitGym versus Sol's 30.3%, 88.0% single-attempt on SRE-Bench binary reverse-engineering versus Sol's 55.9%. During evaluation it discovered and used two previously unknown zero-days, which OpenAI says it disclosed to the maintainers.

The consequence for you: the launch version does defensive work — secure code review, patching — but refuses to build proof-of-concept exploits, and extra safety checks can occasionally stop a legitimate task. In the API a blocked task simply stops. That's upstream model behavior, not a Smart AIPI limit. OpenAI plans to loosen this for vetted defensive workflows through its Daybreak program.

OpenAI also flagged a genuine regression: Astra's written reasoning is harder to monitor than Sol's, because it solves problems in fewer written steps and has more control over its trace. They called it out themselves. It's the right thing to know if your safety story depends on reading chain-of-thought.

The real story: token efficiency, not per-token price

At $10 / $50 per 1M tokens direct from OpenAI, Astra looks expensive next to the 5.6 family — and on a per-token basis it is. But per-token price is the wrong unit. What you pay is tokens burned to finish the task, and this is where Astra is genuinely different:

• On DeepSWE, Astra spends roughly half the output tokens of GPT-5.6 Sol for a higher score.
• On Agents' Last Exam, at the highest-scoring settings, it uses about 65% fewer output tokens than Claude Opus 5.
• Gemini 3.8 Flash (73.8%) and Claude Opus 5 (73.7%) land within a point of Astra on DeepSWE — while being roughly 4–5x less token-efficient per task.
• On Terminal-Bench 4.0, Astra reaches its 57.9% at approximately 9% lower estimated API cost per task than Sol, and 63% lower than Fable 5.1.
• On BenchCAD, Astra hits 95.9% geometric overlap at roughly 43% lower cost than Sol and 86% lower than Fable 5.1.

That is a model whose Pareto curve moved by doing less work, not by charging less. It also creates a real tension for the labs: if each token buys more finished work, a token-metered business either passes the efficiency gain to customers or keeps it as margin. At $10 / $50, OpenAI kept it.

That's the gap Smart AIPI exists to close. We pass the discount through: you get Astra's token efficiency and a 75% lower rate per token.

Pricing: 75% off, same model, same API

Same weights, same OpenAI-compatible API shape, same tools. The only difference is the invoice.

What else shipped with Astra

• Searchable notes in Codex. Instead of compacting a long session into one lossy summary, Astra keeps notes across context windows and can search earlier windows — including tool output that never made it into a summary. Enable it in your Codex config.toml; OpenAI says it becomes the default for Astra in the coming weeks.
• Better handling of ambiguity. Astra fills routine gaps from context and asks focused questions only when the answer would change the outcome. In Codex it can ask asynchronously while continuing unrelated work.
• It stays on task under steering. Earlier models often treated a mid-task correction as a brand-new goal. Astra folds new requirements in without dropping the original constraints.
• Real long context. 100% MRCR v2 retrieval up to 512K tokens, 96.3% in the 512K–1M band.
• Science that landed. On prime gaps, the best known bound on infinitely recurring close prime pairs had moved from 246 to 240; Astra helped establish 186. It also improved a term in a bound on large prime gaps that had stood for over 80 years.

How to use GPT-6 Astra

Chat Completions API

curl https://api.smartaipi.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-6-astra",
    "messages": [{"role": "user", "content": "Refactor this module and add tests"}]
  }'

Responses API with high reasoning

curl https://api.smartaipi.com/v1/responses \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-6-astra",
    "input": "Audit this repo for security issues and open PRs to fix them",
    "reasoning": {"effort": "high", "summary": "auto"}
  }'

Codex CLI, Cursor, Cline, Roo Code, OpenCode, Aider

OPENAI_BASE_URL=https://api.smartaipi.com/v1
OPENAI_API_KEY=sk-your-smart-aipi-key
OPENAI_MODEL=gpt-6-astra

Reasoning effort supports low, medium, high and xhigh. Astra scales meaningfully with effort — early partners report that higher effort buys more verification passes and more browser testing on fresh builds, not just longer answers. For long-horizon agentic work, use high or xhigh.

Which model should you actually run?

• gpt-6-astra — computer use, browser automation, terminal-heavy agents, long-horizon refactors, anything where finishing the task unattended is the point. Its token efficiency means the higher rate often nets out cheaper per completed task.
• gpt-5.6-sol — still an excellent frontier coding model and materially cheaper per token. The right default for high-volume agentic coding where you're not paying for Astra's computer-use edge.
• gpt-5.6-terra — the balanced workhorse: near-flagship coding quality at half of sol's price.
• gpt-5.6-luna — bulk work, classification, summarization, cheap high-throughput pipelines.

All four run on the same endpoint and the same key. Switching is a one-line change, so benchmark them on your workload rather than trusting anyone's chart — including this one.

Get started

1. Sign up at smartaipi.com/signup (free credits, no credit card)
2. Create an API key in the dashboard
3. Set your base URL to https://api.smartaipi.com/v1
4. Use model gpt-6-astra

The best agentic and computer-use model published, in production, at a quarter of the list price.