What is GPT-6 Astra, and how is it different from GPT-5.6 Sol?

What the model actually is

Astra is the flagship of the GPT-6 family, announced by OpenAI on September 3, 2026. In the API it is a single model id, gpt-6-astra, with no mini or nano sibling published alongside it at announcement. Input accepts text and images; output is text only.

The headline numbers from the model page are a 1,050,000-token context window, 128,000 max output tokens, and an April 30, 2026 knowledge cutoff. The reasoning.effort parameter accepts five levels: low, medium, high, xhigh, and max. That is two more rungs than most earlier OpenAI reasoning models exposed, and the top ones are where the expensive behaviour lives.

Tool support on the model page is broad: web search, file search, image generation, code interpreter, hosted shell, apply patch, skills, computer use, MCP, and tool search are all listed as supported. The free API tier is listed as Not supported, so there is no way to try it without a paid account.

The price difference, stated plainly

Standard list pricing for gpt-6-astra is 10 US dollars per million input tokens and 50 per million output tokens. GPT-5.6 Sol on the same page is 4 and 20. That makes Astra 2.5x the cost of Sol on both sides of the meter.

Cached input is 1.00 per million, and cache writes are 12.50, which OpenAI states is 1.25x the uncached input rate. That last line is worth reading twice: a cache write is a surcharge, not a discount. The discount only arrives on subsequent cache reads. If your workload writes a large context once and reads it rarely, caching costs you money rather than saving it.

Two multipliers can move the real bill well above list. Prompts above 272K input tokens are billed at 2x input and cache rates and 1.5x output for the entire request, not just the overage. Fast mode is 2x the applicable rates in the API, and the ChatGPT enterprise rate card lists 2.5x for Astra on its side. Batch and Flex run the other way at 50 percent of standard.

For a rough sense of scale: a million input tokens plus a hundred thousand output tokens costs about 15 dollars on Astra and about 6 on Sol. Multiply that by a long-running agent loop that re-reads context on every step and the gap stops being academic.

Where Astra measurably beats Sol

The clearest documented gain is computer use. In OpenAI's latency simulations on OSWorld 2.0, Astra scores 72.6 percent at roughly 40 minutes per task against Sol's 65.7 percent at roughly 75 minutes. That is a better score in about 47 percent less time, which is the part that matters if you pay for wall-clock agent runs.

On Agents' Last Exam, which OpenAI describes as complex professional tasks in real software, Astra reports 59.3 percent against 55.5 percent for Claude Opus 5 and 53.6 percent for Sol, while using roughly 65 percent fewer output tokens than Opus 5 at those settings. Fewer output tokens at a higher price per token is the trade the whole pricing question turns on.

On BenchCAD, reconstructing 3D objects from multi-view renders via generated CAD code, Astra reports 95.9 percent geometric overlap with tools versus 83.3 percent for Sol. On the saturating end, OpenAI reports 98 percent on FrontierMath Tier 4, 99.9 percent on ARC-AGI-3, and 100 percent on ExploitBench.

Separate the fact from the analysis here. The fact is that these are the numbers OpenAI published. The analysis is that vendor-run benchmarks with vendor-chosen comparison settings are a starting point for your own evaluation, not a substitute for it. OpenAI's own footnotes concede configuration differences between its research setup and Codex production, and note that some Claude scores it reports come from a variant with fewer safeguards.

The safety designation that changes your runtime

On September 1, 2026, ahead of the launch, OpenAI published Path to Astra and stated that Astra meets the Critical cybersecurity capability threshold under its Preparedness Framework. It is the first model the company has designated at that level. The stated meaning is that with the right tools and access it can find previously unknown security flaws and develop ways to exploit them across many well-protected systems without a person guiding each step.

This is not marketing framing. In OpenAI's own internal evaluation on 20 high-severity V8 vulnerabilities, the model discovered and used two zero-day vulnerabilities as part of an exploit chain, which OpenAI says it is in the process of disclosing to maintainers. In expert-led assessment it built a browser-compromise chain that escaped a sandbox and a local privilege-escalation chain from an unprivileged user to root.

The practical consequence for ordinary builders is in the section OpenAI titles What this will mean for users. The company states that extra safety checks can sometimes slow, pause, or stop legitimate work, that the system may flag legitimate activity as potential misuse including work not directly related to cybersecurity or tasks where an agent runs for an extended period, and that when the misalignment monitor pauses a task on surfaces like the API, the task will stop.

If you are building an unattended overnight agent, that is a design constraint, not a footnote. Plan for a task that halts partway and needs to resume, and log enough state to make resumption possible.

Who should actually switch, and who should not

Switch if your workload is computer use, browser automation, long multi-step professional tasks, or agentic coding where a run either succeeds or wastes an hour. The documented speed and completion gains attack exactly that failure mode, and fewer retries can offset a 2.5x token price.

Do not switch reflexively for chat, summarisation, classification, or high-volume short-prompt work. Those are where 2.5x lands directly on your bill with the least to show for it, and where GPT-5.6 Terra at 2 and 12, or Luna at 0.20 and 1.20, are the honest comparison rather than Sol.

Writing quality is the one area to test rather than assume. Reporting from launch-week testers describes Astra as a weaker writer than its predecessor, and third-party measurement has been cited to the same effect. That is secondary-source material and we have not verified those measurements independently, so treat it as a prompt to run your own comparison on your own prompts, not as a settled finding.

Be careful with rollout claims. OpenAI's announcement says access began with a limited set of organizations before widening to ChatGPT Plus, Pro, Business, and Enterprise plus the API, Azure, and Bedrock. What you can reach today depends on your account, and the most advanced cybersecurity capabilities are gated separately through the Daybreak program.

Limits of this answer

Every capability number here is OpenAI's own published figure, on OpenAI-chosen benchmarks, at OpenAI-chosen settings. RepoRadar has not run these benchmarks and does not present any of them as independently verified.

Prices change. The rates quoted are the published list rates checked on September 6, 2026, in US dollars, and OpenAI has previously moved a flagship model's price mid-generation. Check the pricing page before you build a forecast on these numbers.

Availability is a moving target during a rollout week, and enterprise, regional-residency, and Daybreak access all follow separate rules. Nothing here should be read as a promise that your account can call this model today.

A useful next action

Take one real task from your existing pipeline, ideally a multi-step one that currently fails or drags, and run it on both gpt-6-astra and gpt-5.6-sol. Record three numbers per run: total tokens in and out, wall-clock time, and whether a human had to intervene.

Compute cost per successful completion, not cost per token. That single change in metric is usually what settles the question, and it is the only comparison that survives contact with a 2.5x price difference.