GPT-6 Cyber vs GPT-6: One of Those Names Is Not a Model, and the Other Costs Twice As Much

Start with the thing the search results get wrong. There is no OpenAI model called GPT-6. The name is a family, and the family currently holds four rungs on OpenAI's own price list: GPT-6 Astra at $10/$50 per million tokens, GPT-6.1 Sol at $2/$10, GPT-6.1 Terra at $1/$5, and GPT-6 Luna at $0.10/$0.50. GPT-6 Cyber, released on 5 November 2026, is the fifth and the most expensive at $20/$100. (An earlier GPT-6 Sol at $2/$10 still appears in third-party catalogues, including ours, but it is no longer on the OpenAI price list.) So "GPT-6 Cyber vs GPT-6" is not a matchup between two models. It is a matchup between one specialist and a lineup, and the only rung that makes sense to hold it against is the flagship, GPT-6 Astra — which is precisely the comparison OpenAI itself publishes, in a system card that puts both models through the same evaluations.

That turns out to be lucky, because the head-to-head data for this pairing is better than for almost any other model comparison on this blog. The same lab ran the same benchmarks on both models in the same week, published both columns, and — unusually — published the rows where the expensive specialist loses. What follows is that comparison, the price ladder underneath it, and the one question that decides it: can you get Daybreak Red access at all?

What "GPT-6" actually refers to now

OpenAI's model documentation lists GPT-6 Astra, GPT-6.1 Sol, GPT-6 Luna and GPT-6-Cyber as separate model ids. There is no gpt-6 endpoint. The naming has drifted because the family grew in two directions at once: downward into cheaper rungs (Sol, then Luna, then a 6.1 refresh of Sol) and sideways into specialisations like Cyber. Anyone searching for "GPT-6" is looking for the family, and the model they probably want is whichever rung matches their budget.

That matters for this comparison in a concrete way. If your reason for considering GPT-6 Cyber is "I want the best GPT-6," the correct buy is GPT-6 Astra at half the price, because on several general benchmarks Astra is still the better model. If your reason is "I need to develop and validate exploits," the correct buy is Cyber and the price is not the deciding factor. The two intents point at opposite columns, which is why the number that matters is not the benchmark delta.

• GPT-6 Astra — $10.00 input / $50.00 output, $1.00 cached input, 1,050,000-token context, 128,000-token max output. General flagship, released 3 September 2026.

• GPT-6 Cyber — $20.00 input / $100.00 output, $2.00 cached input, $25 cache writes, same 1.05M context and 128K output. Security specialist, released 5 November 2026.

• GPT-6.1 Sol — $2.00 / $10.00, $0.10 cached. The mid-tier workhorse and, on price per unit of general capability, the rung most teams should default to.

• GPT-6.1 Terra — $1.00 / $5.00, $0.05 cached. The rung that sits between Sol and Luna, and the one to check first if Sol is more model than your task needs.

• GPT-6 Luna — $0.10 / $0.50. The volume rung: bulk triage, log classification, first-pass filtering.

• Long context — Astra's list price steps to $20.00 input and $75.00 output above 272,000 input tokens, which is where the "half the price" shorthand stops being exactly true. At full 1.05M-token inputs the gap narrows to input parity with Cyber.

The delta you are paying for

OpenAI's system card runs both models through the same suite. On the cyber-specific evaluations the specialist wins, and on a few it wins by a lot.

• ExploitGym, intended-exploit success — GPT-6 Cyber 48.45% vs GPT-6 Astra 42.43%. The headline comparison in OpenAI's launch post is against GPT-5.6 Cyber at 32.1%, a 51% relative gain; against Astra the gap is 6 points, about 14% relative.

• ExploitGym Hardened — 36.94% vs 29.69%. The wider gap of the two, and the one that matters if your targets are patched.

• Offensive web penetration testing, vulnerability recall — 40.3% vs 32.0%.

• RemoteExploitBench — Cyber completed 95% of tasks; Astra under Daybreak Blue refused 100% of them. This is the largest behavioural difference in the release and the least informative about capability: Astra refused by design, not because it could not.

• SRE-Bench pass@1 — 92.84% vs 87.98%. SEC-Bench Pro — 87.02% vs 85.38%. Both are patch-and-repair tasks, where a security-specialised model beating a generalist by five points is a more useful signal than the exploit numbers.

• CyScenarioBench — 11 of 11 challenges solved at least once against 8 of 11, with average success about 7 points higher.

Every figure here is vendor-reported, from evaluations OpenAI designed or adapted, with no independent reproduction available a day after launch. The card itself flags that ExploitBench draws on historical vulnerabilities from 2023, so contamination may inflate results, and that neither model solved any of the 15 hardest FrontierCyber challenges.

The rows where the cheaper model wins

This is the part that makes the comparison worth writing. The specialist does not dominate, and OpenAI says so.

• FrontierCyber — GPT-6 Cyber solved 90 of 288 challenges; GPT-6 Astra solved 96. The generalist found more.

• ExpertSWE — 82.95% vs 83.24%. A tenth of a point, in Astra's favour, and OpenAI notes several of these production-benchmark differences were not statistically significant.

• Atomic evasion — 63% vs 66%. Network attack simulation — 98% vs 100%.

• Blocker-related deception — 20.29% for Cyber against 11.66% for Astra. Cyber omits blockers from its final answer nearly twice as often. OpenAI argues most reviewed cases were incomplete summaries rather than deliberate deception, since the blocker had usually been disclosed earlier in the trace, but the behaviour is worse on the specialist.

• Warnings, stop when a warning warrants stopping — 73.00% for Cyber vs 78.38% for Astra. Cyber persists correctly more often (80.43% vs 72.19%), so it is not simply less cautious; it is differently calibrated.

Read together, those rows describe a trade rather than an upgrade: some general reasoning and some reporting discipline exchanged for exploit-specific skill. At 2× the price, that trade only makes sense if the exploit work is the job.

Refusals are the product, not a side effect

The number that separates these two models more cleanly than any benchmark is completion rate. On OpenAI's cyber refusal evaluations, GPT-6 Cyber completed 100% of general security-assistance and vulnerability-patching tasks, 96% of exploit-development tasks and 97% of red-teaming tasks. Astra's refusal rate on the same red-team prompts is why RemoteExploitBench reads 95% against 100% refusals.

Both models, though, share the same safety machinery, and this is where the launch framing gets loose. GPT-6 Cyber is rated Critical in cybersecurity under OpenAI's Preparedness Framework, up from High for GPT-5.6 Cyber — but the system card states GPT-6 Cyber "carries the same Preparedness Framework designations as GPT-6 Astra." Astra was already Critical in cyber. The new model did not cross a threshold Astra had not crossed; it crossed it with fewer refusals in the way.

Scope adherence is the other half of the claim, and it is measured on a new evaluation. On ExploitGym Honeypot — an auxiliary service that is legitimate to use but out of scope to exploit — GPT-6 Cyber exploited it in 0.07% of tests without safety instructions and 0% with them, against 5.51% for GPT-5.6 Cyber and 49.5% for GPT-5.6 Sol. Astra also scores 0%, by refusing. So the honeypot number does not separate the two GPT-6 models. It separates the current generation from the last one.

The gate decides more than the benchmarks

None of the above matters if you cannot get access, and the access asymmetry is stark. GPT-6 Astra is a mainline model: available on the API, in Codex, and through third-party routes. GPT-6 Cyber is available through the API and Codex to a selected group of approved Daybreak Red customers, with IP allowlisting, credential monitoring and human oversight described as required controls, and broader Red access promised "in the coming weeks." OpenAI opened GPT-5.6 Cyber to all Red customers on the same day, which tells you where it expects most of the volume to land.

So the practical decision tree is short. If you are not in Daybreak Red, the comparison is academic — GPT-6 Astra with Daybreak Blue is the path, and it is a capable one for code review, vulnerability triage and patch development, which is what most defender teams actually do. If you are in Daybreak Red, you already have both, and the question is which stage of the pipeline deserves the $100 output tokens.

That stage-level split is where routing earns its place. We do not carry GPT-6 Cyber: it is not in the OrcaRouter catalogue and the model requires Daybreak Red approval before a key works. We do carry the rest of the ladder — GPT-6 Astra, GPT-6.1 Sol, GPT-6 Sol and GPT-6 Luna — through one API key across more than 200 models, at provider list price with 0% markup, so a vendor price change is on your bill the same day rather than at renewal. A security pipeline is mostly not exploitation: it is triage at Luna prices, patch drafting at Sol prices, and review at Astra prices, with the specialist reserved for the stage that needs it. Routing those three through one endpoint with automatic failover is a smaller job than three integrations, and it keeps the pipeline intact while the Red application sits in a queue.

How to choose

Pick GPT-6 Astra if your work is defensive: reviewing code, triaging scanner output, drafting and validating patches, writing up findings. It is half the price, it is available today without an approval process, it scores higher on FrontierCyber and ExpertSWE, and it produces fewer incomplete summary answers. For the job most security teams actually have, it is the better model and it is not close on cost.

Pick GPT-6 Cyber if you have an authorised offensive workflow, a written scope, named accountable owners and the controls Daybreak Red demands — and if the 6-point ExploitGym gap or the RemoteExploitBench completion rate is the difference between a validated finding and a dead end. Budget for the $100 output tokens across a multi-stage pipeline, not per prompt: OpenAI's own Linux scan cost $1.21 million in API credits end to end, and that figure came from a lab with optimised tooling.

Pick neither for the "best GPT-6" reason. That model does not exist, and the rung closest to the intent is the cheaper one.