OpenAI Astra vs GPT-5.6 Sol

Astra costs twice as much as GPT-5.6 Sol on input and 67% more on output, for the same context window. At Layer3Labs, we integrate these models into client workflows, and a price gap that size has to be earned by a task the cheaper model actually fails.

OpenAI published one direct evaluation of the two, finding Astra stronger at vulnerability identification and exploit development while using fewer tokens. That result delayed Astra's own launch by five weeks.

Both models are now buyable, so the comparison finally settles something. GPT-6 Astra runs $10 and $50 per million input and output tokens, against $5 and $30 for Sol, and the tokens buy the same 1,050,000-token window on either side.

OpenAI Astra vs. GPT-5.6 Sol: Side-by-Side

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What Each Model Costs to Run

GPT-6 Astra costs $10 per million input tokens and $50 per million output. GPT-5.6 Sol costs $5 and $30, so Astra doubles the input rate and adds two thirds to the output rate for work of the same size.

The context window does not change with the price. Both models hold roughly 1,050,000 tokens, so a document set that fits in one Sol call fits in one Astra call, and a migration between them is not a way to stop chunking anything.

That leaves capability as the only thing the extra money buys. On a workload Sol already completes correctly, Astra returns the same answer for twice the input cost, which is why the upgrade decision belongs to specific tasks rather than to a whole stack.

• GPT-5.6 Sol: $5 per million input tokens and $30 per million output, on OpenAI's published rates.
• GPT-6 Astra: $10 per million input tokens and $50 per million output, so a job costs roughly double to feed and 67% more to generate.
• Both reach the API without a waitlist, so an evaluation can run on both the same afternoon.
• Astra also reaches ChatGPT Plus, Pro, Business and Enterprise seats, which is where most non-developer use of it will happen.

→ Compare per-token pricing for every AI model

What OpenAI Has Published About Each Model

Both models now ship with the same documentation set: per-token pricing, a card, and benchmark figures. Sol's arrived at general availability in July 2026, per OpenAI's GPT-5.6 Sol announcement, and Astra's arrived at its September 3 release.

The evidence behind each is different in kind. Sol has ordinary benchmark figures that place it against other models. Astra has three near-ceiling frontier scores, FrontierMath Tier 4 at 98%, ARC-AGI-3 at 99.9%, and ExploitBench at 100%, plus ten open mathematics problems solved for roughly $2,000 of compute at Sol rates in August 2026.

That difference matters when you try to rank them. Astra has no MMLU, GPQA, or SWE-bench result and no third-party evaluation, so the two models share no common test, and the only head-to-head number is OpenAI's own security comparison below.

• Sol — pricing, a model card, benchmark figures, and API features, all published at general availability.
• Sol capability — max and ultra reasoning modes, with ultra spawning subagents to split complex work inside one request.
• Astra — pricing, a system card, and three frontier scores, plus ten formal proofs with Lean 4 certificates and a 249-page manuscript anyone can check.
• Astra capability — computer use, browsing, software engineering and long multi-step workflows, by OpenAI's own account.
• The overlap — Sol already spawns subagents inside a request, so Astra extends an existing direction rather than opening a new one.

The One Direct Comparison OpenAI Ran

OpenAI compared Astra against GPT-5.6 Sol on cybersecurity work. Astra won both measures. It identified vulnerabilities better, developed exploits better, and used fewer tokens doing it.

That result carries more weight than a leaderboard entry would, because it cost OpenAI its launch. The evaluation on August 7, 2026 placed Astra at the Critical cybersecurity capability threshold, the highest level in OpenAI's Preparedness Framework. No model had been put there before.

OpenAI then stopped two weeks of deployment-focused reinforcement-learning training, held its largest planned frontier run, and began rewriting the Preparedness Framework, which it told Axios on August 18, 2026. OpenAI does not do that over a model that performs like Sol with better margins.

• Astra beat Sol on vulnerability identification. The gap is real on at least one hard reasoning task.
• Astra beat Sol on exploit development, which is the finding that triggered the Critical classification.
• Astra used fewer tokens for both, so the gain is cost as well as capability.
• Sol stays below the Critical threshold and generally available. Nothing here changed what you can run today.

The Fields This Comparison Still Cannot Fill

Three fields a buyer would compare are still blank on the Astra side: rate limits, latency, and standard evaluations. Price and context window were filled in at release, which leaves throughput and like-for-like scoring as the gaps.

No third party has filled the evaluation gap either. Every Astra score in circulation is OpenAI's own, so the only independent read available is the one you generate by running your own test cases through both models.

That makes a capability ranking impossible on published data. Astra is ahead on the one dimension OpenAI measured directly, and unmeasured against Sol on everything a normal workload consists of.

• Rate limits are unpublished, so a team sizing a high-volume pipeline has nothing to plan capacity against.
• Latency and throughput figures are unpublished, and those decide more production designs than an eval score does.
• No MMLU, GPQA, or SWE-bench result exists for Astra, so it appears on no leaderboard beside Sol, Claude Fable 5, or Gemini.
• No third-party evaluation has been published, so every comparison but this one runs on the vendor's own numbers.
• The three scores that do exist sit at 98%, 99.9%, and 100%, close enough to their ceilings that they cannot separate Astra from what comes next.

What to Run Instead of Astra

If the work you had in mind for Astra is hard reasoning, long-horizon research, or security analysis, five models can do a version of it. Picking among them is mostly a cost decision, because the capability gaps at the top are narrower than the price gaps.

GPT-5.6 Sol is the nearest substitute at half Astra's input rate, and its ultra mode already splits work across subagents. Claude Fable 5 is the strongest alternative for long document reasoning and contract work, on the published context and pricing we compare in our GPT-5.6 vs Claude Fable 5 breakdown. Claude Mythos 5 sits alongside it where routing to a different line is cheaper, and Gemini is worth testing where your data already sits in Google Workspace.

Most teams should look one tier down instead. GPT-5.6 Terra at $2 and $12 per million tokens handles support, internal tools, and document analysis. Luna at $0.20 and $1.20 covers drafting and routine automation for a fiftieth of what Astra charges to read the same input.

• GPT-6 Astra — $10 and $50 per million tokens. Worth it on work that runs for hours and that the cheaper tiers fail to finish.
• GPT-5.6 Sol — $5 and $30 per million tokens. Half the input cost, the same context window, and subagents already built in.
• GPT-5.6 Terra — $2 and $12. The right default for support, internal tools, and document analysis at volume.
• GPT-5.6 Luna — $0.20 and $1.20. Fast and cheap enough that most routine automation should start here.
• Claude Fable 5 — the strongest alternative for long document reasoning, contract review, and legal work.
• Claude Mythos 5 — the sibling line worth routing to when Fable 5 is more model than the task needs.
• Gemini — worth a test where the documents and mail already live in Google Workspace.

Who Should Not Wait for Astra

If your work is support triage, extraction, drafting, or routine automation, do not move it to Astra. Those jobs are already handled by Terra and Luna at a fraction of Sol rates, and Astra costs double Sol on input, so the same task answers no better and bills far more.

Teams running regulated data have a second reason to stay put. A model classified at the Critical cybersecurity threshold attracts more review than a lower tier, and both shipped Astra versions withhold their most advanced cybersecurity capabilities, so a security evaluation is testing a deliberately limited model.

One group has a real reason to move: long-horizon research, hard engineering problems, and security analysis. Those are the jobs where a task runs for hours and several agents share it, and where a completed answer is worth $50 per million output tokens.

• Support and ticket triage — stay on Terra or Luna, where cost per task decides the bill.
• Document extraction and summarisation — context handling matters more than reasoning depth, and both models hold the same window.
• Regulated workloads should budget review time before migration time, since a Critical classification means more paperwork rather than less.
• Long-horizon research, hard code, security work — the one group that should keep watching.

The Verdict

GPT-5.6 Sol stays the default, because it costs half as much on input and holds the same context window. Sol has a model card and published evaluations, and its ultra mode already splits complex work across subagents, which covers a large share of what Astra is sold for.

On the one dimension OpenAI measured directly, Astra is ahead. It beat Sol at vulnerability identification and exploit development while using fewer tokens, and OpenAI thought that gap large enough to pause its own training and delay the launch by five weeks.

Astra earns its rate on work Sol does not finish. Long-horizon jobs that run across several agents, and computer-use workflows with many steps, are where the extra $20 per million output tokens buys a completed task rather than a slightly better sentence.

What would change this verdict: a third-party evaluation showing Astra ahead on ordinary business documents, or an OpenAI price cut of the kind Luna and Terra got in July. Neither has happened, so route by task. Test Luna and Terra on your real work first, since most workloads never needed Sol either.