Codex rejects gpt-6.0-astra on ChatGPT accounts, so most solo devs cannot run it yet
UK AISI measured autonomous task horizon at 30.9 minutes against 3.6 minutes for GPT-5.6 Sol
Artificial Analysis puts cost per task between 0.46 USD at low effort and 1.67 USD at max
Cached input is 1.00 USD per 1M against Claude Fable 5.1 at 0.25 USD, four times the price
Pro at 200 USD gets 200 messages a week, and the quota is the live complaint, not the price
Independent intelligence index did not move, 61 for Astra and 61 for the model it replaces
I spent this morning trying to actually use GPT-6 Astra rather than read about it. That turned out to be the most useful thing I could have done, because the first thing you learn is that you probably cannot.
Where You Can Actually Run It Today
Point Codex at it with a ChatGPT account and you get this back:
{"type":"invalid_request_error","message":"The 'gpt-6.0-astra' model is
not supported when using Codex with a ChatGPT account."}
Note the model id in that error is gpt-6.0-astra. The API documentation says gpt-6-astra. Two different strings for the same model, one day after launch.
The API has it. A limited set of organisations in OpenAI's cyber program have it. Codex, for the people who actually live in Codex, does not have it yet. Scanning the developer subreddits on day one turns up benchmark threads and access complaints and close to zero posts from anyone who has run it on real work. One commenter put it plainly: benchmarks and blog posts, no actual user experiences.
There is a trap worth knowing before you try. Setting model = "gpt-6-astra" in ~/.codex/config.toml before you have access makes every Codex invocation fail, not just the ones you intended for Astra. If you have already done that and Codex stopped working, that is why.
OpenAI is issuing one banked usage reset per day of missing access on paid plans. Delivery has been patchy.
The 30.9 Minute Number Is the Real Story
Strip out the AGI quotes and one measurement carries the launch. The UK AI Safety Institute put Astra's autonomous task time horizon at 30.9 minutes, against 3.6 minutes for GPT-5.6 Sol. That is 8.6x, it is independent rather than vendor-reported, and it describes the thing that actually decides whether an agent finishes your work or strands it halfway.
A second data point from a hobbyist harness makes the same shape concrete. Running Pokemon from screenshots only, no memory reads and no walkthrough, Astra reached Champion in 18 hours 12 minutes. GPT-5.6 Sol took 96 hours 35 minutes. GPT-5.5 had not finished after 218 hours.
Time horizon is the metric that matters for one-person work and almost nobody leads with it. A model that is 3 percent smarter and holds a task four times longer changes your day. A model that is 10 percent smarter and still loses the thread at minute four does not.
What Long Horizon Buys a One Person Shop
The practical translation is fewer babysitting checkpoints. At a 3.6 minute horizon you structure work into small verifiable chunks because anything longer degrades. At 30.9 minutes you can hand over a migration, a test suite backfill, or a multi-file refactor and go do something else.
The long context result backs this up. On the 512K to 1M band of OpenAI's recall test, Astra holds 96.3 percent against Sol's 73.8. Long sessions are where solo agent work actually lives, and most models advertise a big window then fall apart in the top half of it.
Browser and computer use moved too. ScreenSpot-Pro goes from 76.9 to 92.7 percent with no tools. If you have been watching agentic browsers, Claude Cowork opening its own browser covers the other side of that race.
The Cost Maths, Worked
Artificial Analysis measures cost per task at 0.46 USD at low reasoning effort, scoring 57 on their index, and 1.67 USD at max effort, scoring 61. That is a 3.6x cost spread inside one model for a 4 point quality gain.
That spread is the single most actionable number here. Most people will set effort to max, pay 3.6x, and get four points. For routine work, low effort is the default that makes economic sense, and you escalate deliberately.
Token price is 2.5x GPT-5.6 Sol at 10 USD in and 50 USD out per 1M. Astra offsets that by using roughly one third of Sol's tokens and one fifth of Opus 5's in the Codex harness. Net effect on coding tasks: cheaper per completed task than Fable 5, more expensive than Sol on general work. The full price table and the tier breakdown are in GPT-6 Astra costs 2.5x Sol.
The Cache Read Gap Nobody Is Pricing
This one decides bills and I have not seen it mentioned anywhere.
Astra cached input costs 1.00 USD per 1M tokens. Claude Fable 5.1 charges 0.25 USD. Four times the price on the exact token type an agentic loop reads most, because a long-running agent re-reads the same context on every turn.
If your workload is one big shot per session, ignore this. If it is a loop that re-reads a large codebase context fifty times, cache reads dominate your bill and the 2.5x headline number understates the gap badly.
There is a second cliff. Cross 272,000 input tokens and the entire request reprices, not just the overflow: 2x on input and cache, 1.5x on output. A prompt that drifts from 270K to 275K does not cost 2 percent more. It costs roughly double.
The Quota Is the Limit, Not the Price
For anyone on a subscription rather than the API, the binding constraint is message allowance.
Plan
Astra Pro messages
ChatGPT Pro, 200 USD
200 per week
ChatGPT Pro, 100 USD
50 per week, shared with Sol Pro
Business Premium
50 per week
Business Standard
15 per month
GPT-5.6 Sol Pro in web chat was effectively unlimited until Astra shipped. The most upvoted reaction on the Codex subreddit was a developer asking where the efficiency gains went, given usage caps arrived alongside them. Another described Plus as having become a paid trial.
Budget by messages, not by dollars. On the 100 USD plan, 50 messages a week is roughly seven a day.
Where It Did Not Improve
The independent general intelligence score did not move. Artificial Analysis puts Astra at 61 and GPT-5.6 Sol at 61. Claude Fable 5.1 sits at 66.
On their Coding Agent Index, Fable 5.1 running in Claude Code scores 70 and Astra in Codex scores 67. Every comparison article on page one dodges that number. I will not: for general coding agent work today, Claude is still ahead on the independent measure, and Astra's advantage is specifically long-horizon autonomy and computer use.
Artificial Analysis also found a reduction in presentation quality, where GPT-5.6 Sol at max still leads every model. If you generate client-facing output, that is a real regression rather than a rounding error.
Refusals tightened hard. Cyber jailbreak refusal went from 59 percent on Sol to 91.5 percent on Astra, and developers doing game development involving fictional security threats are already reporting friction. One said they moved to Codex because Claude refused game work and now expects to move back. I wrote up the full safety picture in OpenAI made Astra harder to watch.
The Demo That Got Audited
The best piece of scrutiny on launch day came from a Reddit user who checked OpenAI's own Form 1040 computer-use demo.
At a taxable income of 36,700 USD the correct liability is 4,169 USD. Astra produced 4,165.50 USD. It applied the marginal rate formulas when the IRS requires the tax table for that income band. The document it worked from was not the real IRS PDF either. It was an AI-generated HTML rendering served from a local web server.
The error is 3.50 USD and it is completely disqualifying for the use case, because a tax figure that is nearly right is wrong. It also went unnoticed in a demo OpenAI chose to publish. That is the honest counterweight to the AGI framing: the capability is real, the output validation is not there yet, and neither is the habit of checking.
Bottom Line
If you are a solo builder, three things follow. You probably cannot run it in Codex yet, so do not restructure your workflow this week. When you can, the reason to switch is long-horizon autonomy and computer use, not raw intelligence, because the independent index says intelligence did not move. And set reasoning effort deliberately, because the gap between 0.46 USD and 1.67 USD per task buys four index points.
The routing answer I am giving myself: Claude stays the default for general coding, Astra gets the long unattended jobs once Codex access lands. That may flip. It has not yet. The comparison I ran on the Anthropic side is at Fable 5.1 vs Opus 5, and the rest of the model work lives in the RAXXO Lab overview.
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