Introducing India cross-Region inference for OpenAI GPT-5.6 models on Amazon Bedrock Amazon Bedrock now supports OpenAI GPT-5.6 models Terra and Luna in India with India geographic cross-Region inference, enabling local data processing for financial services, healthcare, and the public sector. The models offer a 1-million-token context window and process text and image inputs, with requests routed only within India across the Mumbai (ap-south-1) and Hyderabad (ap-south-2) Regions. Users can access the models via inference profiles in.openai.gpt-5.6-terra and in.openai.gpt-5.6-luna using the bedrock-runtime endpoint. Artificial Intelligence /blogs/machine-learning/ Introducing India cross-Region inference for OpenAI GPT-5.6 models on Amazon Bedrock Amazon Bedrock now supports the OpenAI GPT-5.6 models, Terra and Luna, in India, with India geographic cross-Region inference. If you have local data processing requirements in India, including in financial services, healthcare, and the public sector, you can now use these OpenAI models at scale. Amazon Bedrock processes inference requests and data within India. Both models offer a 1-million-token context window, accept text and image input, and produce text output. Your applications can process long documents, large code bases, and mixed text-and-image workloads in a single request. The processing never leaves the country. In this post, we walk through how India geographic cross-Region inference works from the Mumbai and Hyderabad Regions. We also show how to get started from the Amazon Bedrock console and with code, using the OpenAI Responses API, OpenAI Chat Completions API, and the Amazon Bedrock Converse API. India geographic cross-Region inference Cross-Region inference automatically routes inference requests across multiple AWS Regions to help improve throughput, without you having to manage capacity in each Region yourself. It’s primarily a capacity mechanism. Instead of being bound to one Region’s capacity, your requests draw on a broader pool of compute. That helps you maintain throughput and consistent performance under load, which matters most during traffic peaks. With India geographic cross-Region inference, Amazon Bedrock routes requests only within the India geography across Regions such as Asia Pacific Mumbai Region ap-south-1 and Asia Pacific Hyderabad Region ap-south-2 . You can scale to meet demand while keeping data processed within India to meet data residency requirements. You call the profile from either India Region as the source, and Amazon Bedrock routes the request to the destination India Region based on capacity. For the most current information about model availability in each Region, see Regional availability by models https://docs.aws.amazon.com/bedrock/latest/userguide/models-region-compatibility.html in the Amazon Bedrock User Guide. Cross-Region inference works through inference profiles. You call a profile ID as the model, and it defines the model and the AWS Regions Amazon Bedrock can route your request to. The India geographic inference profiles keep that routing within India. There are two profiles: in.openai.gpt-5.6-terra for GPT-5.6 Terra. in.openai.gpt-5.6-luna for GPT-5.6 Luna. With inference profiles, billing and quota consumption are tracked against your account in the source Region, regardless of which backend Region handled the request. Amazon CloudWatch and AWS CloudTrail record log entries in the source Region only, so your monitoring stays in one place. Choosing between Amazon Bedrock Mantle and Runtime endpoints For new applications, we recommend the bedrock-runtime endpoint. It supports the Bedrock-native InvokeModel https://docs.aws.amazon.com/bedrock/latest/userguide/inference-api.html and Converse https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html APIs, the OpenAI-compatible Responses https://docs.aws.amazon.com/bedrock/latest/userguide/bedrock-mantle.html bedrock-mantle-responses and Chat Completions https://docs.aws.amazon.com/bedrock/latest/userguide/inference-chat-completions-mantle.html APIs, and the Anthropic Messages https://docs.aws.amazon.com/bedrock/latest/userguide/inference-messages-api.html API, and it is where Amazon Bedrock features such as Guardrails https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html , intelligent prompt routing https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-routing.html , and cross-Region inference https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html are available. Data residency The India geographic profile keeps inference within India. Requests route only between ap-south-1 and ap-south-2. Your input prompts and output results might move between those two Regions. Data is encrypted in transit across the Amazon network. Amazon Bedrock uses a zero data retention ZDR data security model. This means that by default, Amazon Bedrock does not store model inputs or outputs. However, for certain models, including GPT-5.6, content flagged by the Amazon Bedrock automated abuse-detection classifiers is retained for offline abuse detection. Please see Abuse detection https://docs.aws.amazon.com/bedrock/latest/userguide/abuse-detection.html in the Amazon Bedrock User Guide for more details. Global cross-Region inference Amazon Bedrock also offers global cross-Region inference and global inference profiles prefixed global. in India that route to supported commercial AWS Regions worldwide for maximum capacity. You send your request to the India Region endpoint, either Asia Pacific Mumbai ap-south-1 or Asia Pacific Hyderabad ap-south-2, using the global profile ID as the model ID. Amazon Bedrock then decides which destination Region serves the request. Global cross-Region inference supports OpenAI GPT-5.6 models, including Sol, Terra, and Luna. However, if your workload has local data processing requirements, use the India prefixed in. profiles instead, because they keep inference within the country. To read more about global cross-Region inference, see Introduce cross-Region inference for OpenAI GPT-5.6 models on Amazon Bedrock /blogs/machine-learning/introduce-cross-region-inference-for-openai-gpt-5-6-models-on-amazon-bedrock/ . Access GPT-5.6 models from the Amazon Bedrock console You can try GPT-5.6 in the text playground in the Amazon Bedrock console, which requires no coding or SDK setup. You can send prompts, adjust inference parameters, and switch between variants to get a feel for each model before you integrate the API. - Open the Amazon Bedrock console https://console.aws.amazon.com/bedrock/ in a Region where the models are available, such as Asia Pacific Mumbai ap-south-1. - In the navigation pane, under Test , choose Playground . - Choose Select model in the middle of the page. - Search for OpenAI GPT-5.6 Terra, select IN OpenAI GPT-5.6 Terra , and choose Apply . - Enter a prompt and choose Run to generate a response. Call GPT-5.6 models with the OpenAI Responses API GPT-5.6 models on Amazon Bedrock natively support the OpenAI Responses API format. If your application already calls OpenAI models, you can point your existing OpenAI SDK client at the Amazon Bedrock endpoint in either India Region, Mumbai or Hyderabad, and pass an India geographic inference profile ID as the model parameter. For authentication, Amazon Bedrock accepts either standard AWS credentials or an Amazon Bedrock API key. The API key path is the straightforward fit for the OpenAI SDK, which passes it as the bearer token. For production, generate short-term API keys programmatically with the aws-bedrock-token-generator package. It derives a bearer token from your existing AWS credentials, so no static key needs to be stored. The following example uses Asia Pacific Mumbai ap-south-1 endpoint and India geographic inference profile ID to call OpenAI GPT-5.6 Terra on Amazon Bedrock. The Responses API uses a single input field and returns the generated text in output text , with the output limit set through max output tokens . The same client also works with the Chat Completions API, useful if your application already uses this format. Controlling reasoning depth To control reasoning depth, set the optional reasoning parameter, for example reasoning={"effort": "low"} . GPT-5.6 models on Amazon Bedrock support the following reasoning effort levels: none , low , medium , high , xhigh , and max . For more information, see the related post Get started with OpenAI GPT-5.6 Sol, Terra, and Luna on Amazon Bedrock /blogs/machine-learning/get-started-with-openai-gpt-5-6-sol-terra-and-luna-on-amazon-bedrock/ . Omitting the parameter uses the model default. For the full list of supported parameters, see the OpenAI model documentation in the Amazon Bedrock User Guide https://docs.aws.amazon.com/bedrock/latest/userguide/model-cards-openai.html . Multi-turn conversations with server-side state Set store=True to have Amazon Bedrock retain the response server-side, then reference it on the next turn with previous response id . You send only the new turn. You don’t resend the prior conversation history. If you chain with previous response id , the response you chain from must have been created with store=True . Chaining from an unstored response returns an error. Streaming responses For streaming, set stream=True and iterate over the events: Call GPT-5.6 models with the Converse API If you prefer the AWS SDK and SigV4 authentication over an API key, the Amazon Bedrock Converse API supports GPT-5.6 models with the same unified interface it provides for other models on Amazon Bedrock. ConverseStream covers the streaming case. Prompt caching Your prompts might share a long, stable prefix, such as a system instruction, a knowledge base excerpt, or a set of few-shot examples. When they do, GPT-5.6 models on Amazon Bedrock support prompt caching. Cached reads are billed at a 90 percent discount compared to uncached input tokens. This adds up quickly for Retrieval Augmented Generation RAG and agent workloads that repeat the same context across many turns. You can find details on Amazon Bedrock prompt caching documentation https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html . Prompt caching works with the India geographic inference profiles, so you keep the savings under the India data-residency boundary. Prompt caching runs in two modes, explicit and implicit. With implicit caching, Amazon Bedrock places the cache breakpoints for you automatically. Note that the minimum prefix length is 1,024 tokens. With explicit caching, you mark the cache boundary yourself for precise control, and the cached prefix stays warm for at least 30 minutes. Every response tells you what the cache did, in usage.input tokens details : On the first call, you will see cache write tokens populated as the prefix is stored. On subsequent calls, the same tokens come back as cached tokens , billed at the cache-read rate. Because these counts are part of input tokens rather than added on top, a cached token is counted and charged once. IAM permissions for India geographic cross-Region inference To allow an AWS Identity and Access Management IAM role to invoke GPT-5.6 models through the India geographic inference profile, grant the role access to three resources. These are the India geographic inference profile itself, the foundation model FM in the source Region, and the foundation model in each destination Region listed in the profile ap-south-1 and ap-south-2 . You can use the AmazonBedrockLimitedAccess https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonBedrockLimitedAccess.html managed policy or create your own. The following example grants permission to use the GPT-5.6 Terra model through the India geographic inference profile. The source Region is ap-south-1, and the destination Regions are ap-south-1 and ap-south-2. Replace