Sold by: OpenAI
Deployed on AWS
AWS Free Tier
Use Daybreak Blue to access GPT-5.6 Sol for specialized cyber capabilities for verified defenders conducting advanced, authorized cybersecurity work to Amazon Bedrock.
Overview
OpenAI Daybreak Blue is designed for specialized cybersecurity workflows where teams need to validate, test, and strengthen systems in authorized environments. Security teams often work across vulnerabilities, code, telemetry, alerts, and remediation workflows that require careful reasoning and clear evidence. Using Daybreak Blue teams access GPT-5.6 Sol with safeguards calibrated for defensive cybersecurity work. This supports authorized work such as penetration testing, controlled validation, vulnerability research, and remediation planning, with access designed for specialized cybersecurity workflows that require additional authorization and safeguards. Daybreak Blue helps approved teams move from identifying potential risk to validating exposure, developing fixes, testing remediation, and documenting results for review. It is intended for use on systems, applications, accounts, networks, or data that the customer owns, operates, or is explicitly authorized to test or analyze. Through Amazon Bedrock, organizations can use Daybreak Blue models in the AWS environment where many enterprise security, governance, procurement, and operational workflows already run. Amazon Bedrock gives customers a familiar way to access OpenAI model capabilities through the AWS security, governance, procurement, billing, and operational workflows they already use.
Highlights
- Supports specialized, authorized cybersecurity workflows, including penetration testing, controlled validation, vulnerability research, and remediation planning.
- Helps security teams move from potential risk to validated exposure, tested fixes, and documented results.
- Available through Amazon Bedrock so teams can use OpenAI cyber capabilities within the AWS environment, controls, and procurement path they already rely on.
Details
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/unit |
|---|---|
cache_read_tokens_standard | $0.44 |
input_tokens_global_standard | $4.00 |
output_tokens_global_standard | $20.00 |
cache_read_tokens_global_standard | $0.40 |
input_tokens_standard | $4.40 |
output_tokens_standard | $22.00 |
cache_write_tokens_30m_global_standard | $5.00 |
input_tokens_long_ctx_global_standard | $8.00 |
output_tokens_long_ctx_global_standard | $30.00 |
cache_read_tokens_long_ctx_global_standard | $0.80 |
Dimensions summary
You pay per token used, so cost scales with how much text you send and receive. Dimensions split into input tokens (what you send), output tokens (what the model returns), and cache tokens for reused content. Cache tokens divide further into read and 30-minute write operations. Each of these comes in three variants: standard, global standard, and long-context versions. Global standard applies to distributed processing, while long-context handles larger prompts. Each variant carries its own token rates, letting you match billing to your workload's routing and context needs. There is no upfront commitment; you pay for actual usage.
Top-of-mind questions for buyers
A token is a small chunk of text, roughly a few characters or part of a word. Input tokens count the text you send. Output tokens count the text the model returns. Cache tokens count reused content that is read from or written to a temporary store.
Each dimension bills independently and adds together on one invoice. Input and output tokens usually drive most cost, since every request sends and receives text. Cache read and 30-minute write charges apply only when you reuse prompt content. Long-context and global standard variants meter separately based on routing and prompt size.
Standard meters normal requests. Global standard applies when processing runs across distributed capacity. Long-context applies when your prompt is large enough to need extended context handling. Each variant has its own token rates, so your bill reflects which routing and context path each request uses.
developers.openai.com
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