GPT-6 Cyber

What is GPT-6 Cyber?

GPT-6 Cyber, officially written GPT-6-Cyber, is an OpenAI reasoning model for cybersecurity work. Its API identifier is gpt-6-cyber. OpenAI describes vulnerability investigation, reproduction in an authorized environment and verification of fixes as its intended tasks.

If you searched for GPT-6 Cybe, the documented model name ends in Cyber. Use the complete identifier when checking availability or preparing an API request. This guide covers that specific model rather than treating every GPT-6 variant as interchangeable.

API access and Daybreak Red

Daybreak Red approval alone does not grant GPT-6-Cyber access: additional model approval is required. Confirm eligibility with your OpenAI account team, enable the approved program on the sending project and use a key belonging to that project.

The documented Responses API selection is model: gpt-6-cyber with access_programs.cyber: daybreak_red. API access also requires IP allowlisting. Daybreak Red organizations must enforce SSO and MFA; projects using Zero Data Retention need validated Private Safety Processing.

Read the linked setup instructions for the complete requirements. A parameter in a request cannot grant access to an unapproved organization or project. This website provides reference information and does not provision OpenAI accounts or run Cyber requests.

GPT-6 Cyber pricing per million tokens

Checked on November 6, 2026. OpenAI’s Cyber pricing table lists these Standard short-context rates in USD per 1M tokens. Its short-context category covers up to 272,000 input tokens. The table does not publish long-context rates for gpt-6-cyber; confirm those terms before budgeting larger requests.

Estimate the cost of one review

For a hypothetical request with 10,000 uncached input tokens and 2,000 billed output tokens, the listed short-context token rates give $0.20 input plus $0.20 output, or $0.40 in token charges. This is arithmetic for an illustrative usage scenario, not a measured Cyber request or a total-price quote.

Keep generated output, cache reads, cache writes and separately billed tools in different budget lines. Repeating a prompt locally does not demonstrate a cache hit. Use the usage categories actually reported by the provider, and include retries when evaluating the cost of an accepted result.

Context window and output limits

The official model page lists a 1,050,000-token context window, a 922,000-token maximum input and a 128,000-token maximum output. It lists text and image input with text output, plus Responses and Chat Completions endpoints.

Treat the input cap, output cap and combined context budget as separate checks. A source file that fits by itself does not establish that a conversation containing that file, previous tool results and a requested response will fit. Keep room for the response and verify the assembled request.

GPT-6 Cyber tokenizer and token counting

Tiktokenizer has not verified a local tokenizer or tiktoken encoding mapping for GPT-6-Cyber. This guide therefore does not display an exact Cyber counter, token boundaries or token IDs. Counts from another GPT selection or o200k_base describe that selected method only.

OpenAI documents a Responses input-token counting endpoint for supported structured requests. Check that it accepts your approved Cyber model and payload, and use provider-returned usage for the actual call. This guide has not tested the counting endpoint with a Cyber-approved account.

Include the instructions, conversation, code, tool definitions and supported media you intend to send. Counting a pasted excerpt separately omits the surrounding request. A preflight input measurement also cannot predict the output a future generation will produce.

Plan a security review prompt budget

Start a review package with the affected code, a description of the suspected behavior, the environment and the evidence you already have. Put the desired outcome in concrete terms: for example, identify the inputs that exercise a regression test and explain how the proposed patch changes that path.

Select context by relevance rather than filling the maximum window. Preserve the interfaces, callers and test results needed to assess the change; remove repeated logs and unrelated files. Keep file names and revision identifiers with excerpts so findings can be checked against the same source revision.

Compare results on a fixed task with written acceptance criteria. Record the payload version, actual usage, tool costs and whether a reviewer accepted the result. Token efficiency is useful when the smaller package still supports a reproducible conclusion.

Tiktokenizer Reference Sources

Tokenizer behavior and model limits change. Verify production decisions with current provider documentation:

• OpenAI: GPT-6-Cyber model specifications
• OpenAI: Daybreak setup and Cyber access
• OpenAI: Cyber model pricing
• OpenAI: Counting tokens