OpenAI GPT-6 Astra is the most capable model to date from OpenAI, built for the hardest work businesses need to get done. It combines deeper reasoning and judgment with advances in computer and browser use to help turn open-ended challenges into polished, finished work.
Astra is well suited for complex software engineering, data analysis, professional knowledge work, and long-running agentic workflows. It can help teams investigate issues across large codebases, synthesize information into recommendations, and produce high-quality written work. Stronger writing and design judgment can reduce rewriting and reformatting, while Astra is designed for token-efficient completion of complex tasks.
Through Amazon Bedrock, organizations can use GPT-6 Astra in the AWS environment where many enterprise applications and operational workflows already run. Teams can access OpenAI model capabilities alongside their existing AWS security, governance, procurement, billing, and operational workflows.
Highlights
The flagship OpenAI model for advanced reasoning, coding, scientific research, cybersecurity, and complex agentic workflows requiring computer and browser use.
Designed for teams that need deeper judgment, multi-step execution, and polished results, with an emphasis on token efficiency and less rework.
Available through Amazon Bedrock so organizations can build with OpenAI capabilities inside their existing AWS environment and workflows.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
You pay only for what you use, measured per token. Pricing splits into three token types: input tokens you send, output tokens the model returns, and cache tokens that store or reuse prompt data. Cache splits further into read and write, with 30-minute write windows. Each token type comes in service tiers: standard, priority, and flex, plus global variants that let you route requests across regions. Some dimensions add long-context handling for larger inputs. You mix and match these independent dimensions based on your workload, region routing, and speed needs. No commitment applies.
Top-of-mind questions for buyers
How is one token counted for billing across these dimensions?
A token is a small chunk of text the model processes, roughly a few characters or part of a word. Input tokens cover text you send. Output tokens cover text the model returns. Cache tokens cover prompt data stored or reused. You are billed per token consumed in each category.
What is the difference between standard, priority, and flex service tiers?
These tiers set how your requests are processed. Priority targets faster handling for time-sensitive work. Flex suits cost-sensitive, high-volume workloads that tolerate slower processing. Standard sits between them. Global variants route requests across regions. Each tier is billed as its own token dimension, so you pick per workload.
How do the different token dimensions combine on my bill?
Each token type bills independently and adds together. A single request can incur input, output, and cache charges at once. Long-context dimensions apply when your input exceeds standard size limits. Output tokens usually drive the largest share, since generated text is metered separately from what you send.
developers.openai.com
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