Updated November 10, 2026
What is GPT-6 Cyber?
GPT-6 Cyber is OpenAI's specialist model for investigating vulnerabilities, reproducing issues in authorized test environments and verifying fixes. Its API model ID is gpt-6-cyber. This is not a generally available replacement for GPT-6 Astra: official documentation requires Daybreak Red access plus additional approval for the model, with project access enabled before requests can succeed. The model accepts text and images and produces text. It has a 1,050,000-token context window, a maximum input of 922,000 tokens and a 128,000-token output limit. Those capacity figures describe the model interface, not an unrestricted permission to perform any security task.
Evaluate GPT-6 Cyber if your organization has a verified need for specialist security research and can complete the Daybreak setup. For ordinary coding and broad knowledge work, a general model is the more direct starting point.
Official model and release documentation
Specification | Verified detail |
|---|---|
Provider | OpenAI |
Model ID and snapshot | gpt-6-cyber |
Access | Daybreak Red plus model approval |
Project requirements | Approved organization and enabled project |
Context window | 1,050,000 tokens |
Maximum input | 922,000 tokens |
Maximum output | 128,000 tokens |
Modalities | Text and images in; text out |
Reasoning | Reasoning-token support documented |
Endpoints | Responses and Chat Completions |
Base input/output up to 272K input | $20 / $100 per million tokens |
What changed and how to approach migration
GPT-6 Cyber extends OpenAI's specialist cyber model line rather than renaming a general-purpose assistant. Migration depends on entitlement and project configuration as well as model capability. The Daybreak guide says organizations using older cyber models should confirm access to the replacement before switching. It also distinguishes the model selection from the access-program request field: setting access_programs.cyber selects behavior within approved access and does not grant permission. For GPT-6 Cyber the documented selection is daybreak_red. A working integration with a different OpenAI model does not establish that this model can be called from the same project without additional setup.
Area | What changed |
|---|---|
Model selection | Use gpt-6-cyber after approval |
Access selection | daybreak_red for this specialist model |
Project setup | Enable the exact model and program |
Network setup | Configure the required IP allowlist |
Migration baseline | Compare against the cyber model actually used today |
Capabilities and practical use cases
The intended workflow is evidence-led security analysis: understand the suspected vulnerability, inspect relevant code or context, reproduce the issue where authorized and evaluate a proposed fix. The model can use tools through the Responses API, including hosted shell, code interpreter, file search and function calls. Those tools make a capable investigation environment possible, but they do not remove the need to define the systems and actions within scope. A good deliverable should distinguish an unconfirmed hypothesis, a reproducible vulnerability and a repair that has passed regression checks. General coding quality is not a substitute for that chain of evidence.
Workflow | Practical fit |
|---|---|
Vulnerability investigation | Connect a finding to inspectable evidence |
Authorized reproduction | Check the issue in an appropriate test environment |
Fix verification | Test the repair and likely regressions |
File and code analysis | Use relevant source material and supporting tools |
Structured outputs | Return schema-validated findings and status fields |
Tool integration | Function calling, search and approved execution tools |
Benchmarks and evidence
Official OpenAI documentation describes GPT-6 Cyber as a model for investigating vulnerabilities, reproducing them in authorized test environments and verifying fixes. Its documented tools and capacity establish what an integration can support, not how reliably it will resolve every case. A useful security evaluation should measure whether findings are valid, whether a second engineer can reproduce them and whether the proposed repair passes regression tests. Include plausible false alarms and cases with insufficient evidence, and measure scope adherence separately from task completion.
Evidence | Reported result | How to interpret it |
|---|---|---|
Official model documentation | Limits, prices, endpoints and tools | Product specification evidence |
Daybreak documentation | Access and project setup | Not a performance benchmark |
Local investigation pilot | Finding validity and repair quality | Requires your own representative cases |
Scope adherence | Actions stay within task boundaries | Measure separately from task success |
Pricing and total cost
For requests with up to 272,000 input tokens, Standard rates are $20 per million input tokens, $2 cached input and $100 output. For requests above that input threshold, the documented rates are $40 input, $4 cached input and $150 output. Short-context cache writes are $25 per million tokens for 30 minutes or $50 for 12 hours. One million input tokens spread across short-context requests plus 100,000 output tokens would cost $30 at the lower tier; a single million-token input request falls in the higher tier and the same input/output totals would be $55. Extra tool charges are separate.
Cost component | Published term or planning boundary |
|---|---|
Input up to 272K per request | $20 / million tokens |
Output in that tier | $100 / million tokens |
Cached input in that tier | $2 / million tokens |
Input above 272K per request | $40 / million tokens |
Output above 272K input | $150 / million tokens |
Cached input above 272K | $4 / million tokens |
Short-context cache writes | $25 at 30m; $50 at 12h per million tokens |
Limitations and deployment tradeoffs
Approval is a real deployment dependency, not a checkbox inside a request. Daybreak Red organizations must require SSO and enforce MFA through their identity provider. GPT-6 Cyber API use also requires an IP allowlist; projects using Zero Data Retention need the documented Private Safety Processing setup. The model does not support Batch, fine-tuning or Realtime according to the current model page, so those capabilities should not be assumed from other OpenAI offerings. The long-context pricing boundary is another practical limitation: a prompt that fits the context window can still cross into a materially more expensive input tier.
Consideration | What to check |
|---|---|
Access approval | Confirm program, model and project permissions |
Identity and networking | Complete SSO, MFA and IP requirements |
Retention configuration | Follow PSP requirements when using ZDR |
Unsupported interfaces | Do not assume Batch, Realtime or fine-tuning |
Long-context billing | Check the 272K input boundary before estimating cost |
How to evaluate GPT-6 Cyber for your workload
Start with a small, representative set of approved specialist vulnerability investigation tasks, including ordinary cases, difficult edge cases and tasks with an objectively checkable answer. For this model, a useful pilot includes a labeled set of valid findings, false alarms and patch-verification cases. Keep the prompts, available tools, source material and acceptance criteria consistent across candidates. Record the exact model ID, reasoning configuration, date and any provider-specific access program so the comparison can be repeated later.
Measure completed-task quality rather than the fluency of the response. Ask a reviewer to identify unsupported claims, incorrect changes, missing evidence and avoidable manual repair. An agent that stops honestly when evidence is insufficient can be more useful than one that produces an impressive-looking but invalid artifact. Where tools are involved, track invalid arguments, failed calls and recovery behavior separately from the final answer. A single successful demonstration is a reason to continue testing, not proof of a dependable service level.
Pilot measure | What to record |
|---|---|
Task completion | Was the requested artifact actually completed and checked? |
Evidence quality | Can a reviewer trace decisive claims to sources or tests? |
Correction burden | How much expert time was needed after the model finished? |
Latency | Measure end-to-end time, not just first-token speed |
Total cost | Include tokens, tools, retries and human review |
Failure handling | Did it report uncertainty and recover without inventing success? |
Use a staged rollout only after the pilot meets the target you set in advance. Preserve a known baseline and a way to compare new requests with the previous configuration. If the model, tool interface, access policy or price changes, rerun the affected cases instead of assuming an old result still applies. The purpose of evaluation is a defensible choice for your actual work, not a claim that one model is universally best.
Who should use it, and what should you compare?
The best fit is a security organization that can validate technical findings and manage the specialist access requirements. It is not an economical default for routine extraction, editorial writing or general assistant traffic. A pilot should include known valid findings, plausible false alarms and cases where the correct outcome is to say that the evidence is insufficient. Compare the quality and reproducibility of the final report, not only whether the model produced an exploit description. If your needs are ordinary code review or broad software development, evaluate a general-purpose model first and escalate only when the approved specialist workflow benefits.
Alternative | Why compare it | Decision boundary |
|---|---|---|
GPT-6 Astra | General frontier coding and reasoning | Different access program behavior and score |
GPT-6.1 Sol | General software work | Not equivalent to Cyber entitlement |
Claude Mythos family | Verified specialist comparison | Separate vendor verification |
General code-review tools | Routine triage and patch review | May satisfy the task with less setup |
Frequently asked questions
Does Daybreak Red approval alone enable GPT-6 Cyber?
No. Official documentation requires Daybreak Red plus additional model approval and project access. The account team can confirm entitlement. The request field chooses within that entitlement; it does not create access.
Does GPT-6 Cyber support Responses?
Yes. The model page lists Responses and Chat Completions. Its documented Responses tool surface includes function calling, search, file search, code interpreter, hosted shell and other tools, subject to access and product conditions.
Is the price always $20 input and $100 output?
No. Those Standard rates apply to requests with up to 272K input tokens. Requests above that threshold use $40 input and $150 output per million tokens, with different cached-input rates too.
Can I transfer Astra evaluation results to Cyber?
Not automatically. Astra and Cyber are different models, and Cyber has separate approval and deployment requirements. Compare the same tasks, tools and budgets, with finding validity and scope adherence measured explicitly.
Related model guides
Compare the full AI model directory
Sources and editorial method
Official GPT-6 Cyber specifications and pricing
Daybreak setup and project requirements
Specifications, access conditions and prices were checked against the linked provider sources on November 10, 2026. Provider-reported benchmarks are attributed and are not presented as independently reproduced tests. Missing specifications remain unverified rather than being inferred from a related model. Practical workflow advice is an evaluation recommendation, not a claim that we ran new hands-on trials. Recheck official terms before purchase or deployment.