Quick answer: GPT-6 Astra is OpenAI's frontier model, released September 3, 2026. In the API it is gpt-6-astra: a 1,050,000-token context window, 128,000 max output tokens, an April 30, 2026 knowledge cutoff, text-and-image input, and Standard pricing of $10 per 1M input / $50 per 1M output tokens. Its headline capability is not raw chat quality — it is computer use: driving real software, browsers, and terminals end to end. OpenAI also says it is the first model to meet the Critical cybersecurity threshold under its Preparedness Framework.
If you have spent the last three days reading launch coverage, you have seen the same benchmark table reprinted about forty times. What almost nobody has done is read OpenAI's own appendix to the end — where the company's own numbers show Astra losing three comparisons to Anthropic's Claude Fable 5.1 and Claude Opus 5. This article gives you the full picture, wins and losses, plus the pricing detail that will surprise anyone planning long-context work. If you followed Astra before it shipped, our earlier account of the ten open math problems an internal Astra solved covers the pre-release period this page picks up from.
Credibility note: every specification and score below comes from OpenAI's own launch announcement and its API model documentation, both read on September 6, 2026, plus Artificial Analysis's independent leaderboard for third-party comparison. We have not run our own benchmark suite against Astra, and we label vendor numbers as vendor numbers rather than dressing them up as testing. Prices and rollout status are moving weekly right now — verify against OpenAI's pricing page before committing a budget. This piece was researched and drafted with AI assistance, then fact-checked line by line against the primary sources listed at the end before publication.
What This Article Solves
The pain point is launch-week asymmetry. On day three of a frontier launch, the only detailed numbers in existence belong to the vendor, and the vendor chose which comparisons to publish. Read only the headline and you will conclude Astra beats everything; read only the Reddit threads and you will conclude it is a regression. Neither is true.
By the end of this page you will know Astra's actual specifications, which benchmarks it leads and which it does not, what it costs once you account for two pricing rules that the headline rate hides, and the specific failure mode early users are reporting that no benchmark captures.
GPT-6 Astra at a Glance
| Field | Value |
|---|---|
| Vendor | OpenAI |
| Released | September 3, 2026 |
| API model ID | gpt-6-astra |
| Context window | 1,050,000 tokens |
| Max output | 128,000 tokens |
| Knowledge cutoff | April 30, 2026 |
| Modalities | Text + image in, text out |
| Reasoning effort levels | low, medium, high, xhigh, max |
| Input price | $10.00 / 1M tokens |
| Cached input | $1.00 / 1M tokens |
| Cache write | $12.50 / 1M tokens |
| Output price | $50.00 / 1M tokens |
| Batch / Flex | 50% of Standard rates |
| Fast mode | 2x speed at 2x the applicable rate |
| Platforms | ChatGPT, OpenAI API, Microsoft Azure, AWS Bedrock |
Two pricing rules are easy to miss and both matter more than the headline rate:
The 272K cliff. Requests with more than 272,000 input tokens are billed at 2x the input and cache rates and 1.5x the output rate for the entire request — not just the tokens above the line. A 300,000-token request is not 10% more expensive than a 272,000-token one; it is roughly double on the input side. If you are planning to use the million-token window casually, price it at $20/$75, not $10/$50.
Cache writes cost more than fresh input. Writing to cache is billed at 1.25x the uncached input rate. Caching only pays off when you read the same prefix several times — which, to be fair, is exactly what agentic loops do.
What Makes Astra Different: Computer Use
Every recent frontier release has claimed a coding win. Astra's genuinely distinct claim is operating software the way a person does — clicking, scrolling, filling forms, reading what came back, and deciding what to do next.
OpenAI's reported results on that axis:
| Benchmark | GPT-6 Astra | GPT-5.6 Sol | Claude Opus 5 |
|---|---|---|---|
| Agents' Last Exam | 59.3% | 53.6% | 55.5% |
| OSWorld 2.0 (offline set, partial) | 72.6% | 65.7% | 70.2% |
| ScreenSpot-Pro (no tools) | 92.7% | 76.9% | — |
Source: OpenAI's launch announcement, September 3, 2026.
The efficiency claim underneath those scores is arguably the more useful number. OpenAI reports that at the highest-scoring settings on Agents' Last Exam, Astra uses roughly 65% fewer output tokens than Opus 5, and that in OSWorld 2.0 latency simulations it reaches a higher score in about 47% less time per task than Sol (72.6% at ~40 minutes versus 65.7% at ~75 minutes). For an agent you are paying by the token and waiting on by the minute, "fewer tokens, less wall clock, higher score" is the whole ballgame.
The demos OpenAI chose to lead with are unusually concrete for a model launch — laying out a printed circuit board in KiCad from a schematic, filling in a Form 1040, building a house in Blender and walking through it in Unreal Engine 5, running frontend QA on a site it just built. These are not chat transcripts. They are recordings of software being operated.
The Benchmarks Astra Wins
From OpenAI's published tables (September 3, 2026):
| Benchmark | GPT-6 Astra | GPT-5.6 Sol | Claude Fable 5.1 |
|---|---|---|---|
| FrontierMath Tier 4 (v2) | 97.6% | 83.0% | 87.8% |
| GPQA Diamond | 96.0% | 94.6% | 93.7% |
| Terminal-Bench 4.0 | 57.9% | 37.3% | 55.8% |
| Terminal-Bench Science 0.1 | 64.6% | 22.4% | 52.6% |
| DeepSWE v1.1 | 74.1% | 72.7% | 67.4% |
| AutomationBench | 41.4% | 18.1% | 31.4% |
| BenchCAD (with tools) | 95.9% | 83.3% | 84.3% |
| ExploitBench | 100.0% | 78.5% | — |
| SRE-Bench (1 attempt) | 88.0% | 55.9% | — |
| HealthBench Professional | 63.4% | 60.5% | 58.1% |
OpenAI additionally reports 99.9% on ARC-AGI-3, with ARC Prize Foundation's Greg Kamradt quoted saying Astra surpassed their human action-efficiency baseline on 96% of levels — effectively human parity on that benchmark.
Some of these gaps are enormous. Terminal-Bench Science going from 22.4% to 64.6% in one generation is not an incremental release. Neither is ExploitBench at a perfect 100%.
The Benchmarks Astra Does Not Win
This is the section the launch coverage skipped, and it is drawn from OpenAI's own comparison tables — not from a competitor's marketing.
| Benchmark | GPT-6 Astra | Best competitor shown |
|---|---|---|
| Humanity's Last Exam (with tools) | 57.2% | Claude Fable 5.1 — 65.0% |
| Artificial Analysis Intelligence Index v4.1.1 | 61.2 | Claude Fable 5.1 — 65.7 |
| Artificial Analysis Coding Agent Index v1.4 | 67.0 | Claude Opus 5 — 68.1 |
Three things worth sitting with here.
First, OpenAI published these anyway. A vendor including three losses in its own launch appendix is a mild point in favour of the rest of the table's credibility. Take the wins more seriously because the losses are there.
Second, the independent leaderboard agrees on the direction. Artificial Analysis's live model comparison, checked September 6, 2026, currently ranks Claude Fable 5.1 (max with fallback) first on its Intelligence Index at 57, with GPT-6 Astra (max) second at 55. That is a different index version from the 61.2/65.7 pair OpenAI cited, so the absolute numbers are not comparable — but both the version OpenAI chose and the current live board put Fable 5.1 ahead. When a vendor's own citation and an independent tracker point the same way, that is about as close to a settled finding as launch week gets.
Third, "intelligence index" and "does my job" are different questions. Artificial Analysis's same dashboard puts Astra's cost per Intelligence Index task at $2.57 against Fable 5.1's $6.12 — Astra scores marginally lower and costs roughly 2.4x less per task, because it spends far fewer tokens getting there. Which of those two facts matters more depends entirely on whether your bottleneck is quality ceiling or budget.
Cybersecurity: The Part With Real Restrictions
Astra is the first OpenAI model to meet the Critical cybersecurity threshold under the company's Preparedness Framework, and the deployment reflects that.
What OpenAI reports it can do, tested without production safeguards: a perfect 100% on ExploitBench, 42.4% on ExploitGym (versus 30.3% for Sol), and 88.0% single-attempt on SRE-Bench binary reverse engineering. On an internal benchmark built from vulnerabilities disclosed between June and August 2026 — chosen to rule out memorisation — Astra scored 39.0% against Sol's 5.5%, and OpenAI says it discovered and used two previously unknown zero-day vulnerabilities during the evaluation, both since disclosed to their maintainers.
What the shipping model will actually do for you is narrower. Astra as deployed will help with secure code review and patching. It will refuse more advanced work including writing proof-of-concept exploits. OpenAI says it plans to relax those safeguards for vetted users through a program called OpenAI Daybreak "in the coming weeks."
Practical consequence: if you are a security professional evaluating Astra for offensive-adjacent defensive work today, expect refusals, and expect API tasks to be stopped outright rather than paused — OpenAI states that when its misalignment monitoring intervenes, ChatGPT and Codex ask you to review, while the API simply halts the task.
What Early Users Are Actually Reporting
Benchmarks measure ceilings. They do not measure whether a model is pleasant to work with, and the first week of community feedback has been notably split on exactly that.
The most-discussed thread on r/OpenAI in Astra's first week is titled "Astra (GPT-6) High Intelligence, Low Intuition". A developer who had been using GPT-5.6 Sol daily described a specific, repeatable friction: Astra proposed new infrastructure for a problem the existing architecture already solved, then began implementing before the approach was agreed. Across a few hours they hit roughly four instances of the model missing intent, over-scoping a solution, or starting work without establishing a shared plan. Replies echoed the pattern — exceptional at problems Sol could not crack, occasionally over-engineering to the point where the output was worse.
Treat this as a pain-point signal, not a measurement. It is a handful of developers in week one, it is not controlled, and it directly contradicts OpenAI's own claim that Astra "asks focused questions when the answer could change the outcome." Both can be partly true: a model that is better at judging when to ask can still be worse for a user whose workflow depended on being asked constantly.
The actionable version: if you are migrating from Sol or Claude, make your plan-before-acting expectation explicit in the system prompt rather than assuming the model shares your predecessor's defaults. That is a five-minute fix for the single most-reported complaint.
You can test this yourself without a migration. Run your hardest real task through GPT-6 Astra on glm5.app alongside whatever you use today — same account, same credit balance, no separate API key — and compare the transcripts rather than the benchmark tables.
Where to Get It
ChatGPT. Rolling out to Plus, Pro, Business, and Enterprise. Usage counts against existing subscription allowances, with additional credits purchasable. Pro, Business, and Enterprise plans also get GPT-6 Astra Pro. One detail that trips up teams: for Enterprise workspaces, access is off by default at launch and an administrator has to enable it.
OpenAI API. gpt-6-astra, available through both Chat Completions and the Responses API, with support for streaming, function calling, structured outputs, web search, file search, code interpreter, hosted shell, apply patch, computer use, and MCP. Fine-tuning is not supported.
Microsoft Azure and AWS Bedrock. Both listed as launch platforms.
Multi-model platforms. If you want Astra and its closest rival in the same window without two vendor accounts, aggregators carry both — including glm5.app, where GPT-6 Astra and Claude Fable 5.1 sit in the same model picker on one credit balance.
Is GPT-6 Astra Worth It?
Use it when: your work is computer use or browser automation (this is its clearest lead, and the token-efficiency gap makes it the cheapest way to get frontier agent behaviour); you are doing terminal-heavy engineering or scientific workflows; you need frontier math or graduate-level science reasoning; or you are running high-volume agentic loops where output-token count dominates the bill.
Look elsewhere when: you need the top of the independent intelligence leaderboard for one-shot hard reasoning — Artificial Analysis currently puts Claude Fable 5.1 above it; your workload routinely exceeds 272,000 input tokens, where the 2x/1.5x surcharge changes the arithmetic badly; you need offensive-security tooling that the shipping safeguards refuse; or your team's workflow depends on a model that plans out loud before acting, which is the friction early adopters report most.
The honest summary: Astra is a genuine step change in computer use and cyber capability, a modest step in raw reasoning, and — on independent measurement — not the smartest model available. Those three sentences are all true at once, and the launch coverage that only tells you the first one is selling you something.
Two places to go next: the full head-to-head against Claude Fable 5.1, which is the comparison the pricing parity makes genuinely difficult, and the setup guide covering ChatGPT, the API, and Codex if you have already decided.
Frequently Asked Questions
Is GPT-6 Astra free? Not on its own. There is no free tier for Astra in the API, and rate limits start at Tier 1 (Free is listed as "not supported"). In ChatGPT it is included within existing Plus, Pro, Business, and Enterprise allowances, so it is "free" only in the sense that it is bundled into a paid subscription you already have.
Is GPT-6 Astra AGI? No, and OpenAI does not claim it is. The company's framing is "the world's most intelligent and aligned model." The ARC-AGI-3 result — 99.9%, with human parity on action efficiency according to the ARC Prize Foundation — is the strongest single data point people point to, but saturating one benchmark designed to probe novel-environment learning is not the same as general intelligence. Independent leaderboards still rank a competitor above it on reasoning.
What is GPT-6 Astra's context window? 1,050,000 tokens, with 128,000 max output tokens. Note the pricing cliff: above 272,000 input tokens the whole request is billed at 2x input and cache rates and 1.5x output.
What is the GPT-6 Astra parameter count? OpenAI has not disclosed it. No parameter count, architecture description, or training-compute figure appears in the announcement or the model documentation. Anyone quoting a specific number for Astra is guessing.
How does GPT-6 Astra compare to GPT-5.6 Sol? It leads Sol on every benchmark OpenAI published, several by very wide margins (Terminal-Bench Science 64.6% vs 22.4%; Terminal-Bench 4.0 57.9% vs 37.3%; ExploitBench 100% vs 78.5%). Sol remains relevant mainly for cost and for users who prefer its more consultative working style — a preference several early Astra adopters have said out loud.
When was GPT-6 Astra released? September 3, 2026, alongside a safety overview and system card published the same day. Rollout is staged: a limited set of organizations first, then all ChatGPT paid tiers "over the coming days."
Sources
- GPT-6 Astra: A new generation of intelligence — OpenAI — the launch announcement, September 3, 2026: capability claims, full benchmark tables, alignment evaluations, availability, and Standard pricing.
- GPT-6 Astra model reference — OpenAI API docs — context window, max output, knowledge cutoff, the full per-token rate card including cache and the 272K threshold rule, effort levels, endpoints, and rate limits.
- Artificial Analysis — model comparison — independent Intelligence Index, output speed, and cost-per-task figures, checked September 6, 2026.
- Introducing Claude Fable 5.1 and Claude Mythos 5.1 — Anthropic — the competitor numbers used for cross-checking OpenAI's comparison table.
Community discussion referenced above is used only as a signal about user experience, never as a source for specifications or scores. All figures verified September 6, 2026; a model three days into a staged rollout changes fast, so confirm current pricing and availability with OpenAI before you build a forecast on them.
