Teams are building AI into the places where work already happens, across documents, spreadsheets, presentations, software systems, tools, and business workflows. GPT-5.4 brings frontier reasoning, strong coding performance, computer-use capabilities, and long-context support to applications that need to operate across those environments.
GPT-5.4 helps developers build AI applications and production workflows that can interpret context, interact with tools, operate software environments, and verify outputs across multiple steps. It is well suited for professional workflows that require reliable reasoning and action across complex business systems.
Through OpenAI APIs and Amazon Bedrock, organizations can build reliable AI applications and production workflows with GPT-5.4s frontier reasoning capabilities and the AWS controls they already use.
Highlights
Access GPT-5.4 for frontier reasoning, coding, computer use, document and spreadsheet workflows, presentations, software operation, and professional knowledge work.
Build AI applications and agents with long-context reasoning, tool use, computer interaction, and workflow execution across complex environments.
Support more reliable professional workflows with a model designed to plan, execute, and verify complex tasks, while using Amazon Bedrock for AWS-native access, governance, billing, and eligible commitment drawdown.
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 per token, with no upfront commitment. Charges split into input tokens, output tokens, and cached input tokens. Each token type is billed across four service levels: standard, priority, flex, and batch. Separate rates apply to long-context requests. Many dimensions carry a region prefix (for example USE1, EUC1, APS1), so pricing varies by AWS Region and by global routing. Cache-read tokens are billed separately in select regions. Your total cost scales with token volume, the service level you choose, whether long context is used, and the Region where you run requests.
Top-of-mind questions for buyers
What counts as one token unit for billing input, output, and cached input?
A token is a small chunk of text the model processes, roughly a few characters. Input tokens cover text you send. Output tokens cover text the model generates. Cached input tokens cover reused prompt content that the system stores to skip reprocessing. Each type meters separately.
How do the standard, priority, flex, and batch service levels change what I pay?
Each token dimension is billed at one of four service levels. Standard is the default rate. Priority handles latency-sensitive requests. Flex suits workloads that tolerate variable timing. Batch groups requests for asynchronous processing. You pick the level per request, and its rate applies to the tokens used.
How do input, output, and cached token charges combine on my bill?
All three charge independently and add together on the same invoice. Output tokens often drive the largest share since generated text can exceed the prompt. Cached input tokens reduce cost when you reuse prompt content. Long-context requests and your chosen Region also affect which rate applies.
developers.openai.com
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