GPT-6.1 Terra

OpenAI — GPT-6.1 Terra

Model Details

As the newest model in the Terra line, GPT-6.1 Terra combines detailed visual reasoning, accurate computer use, and improved factuality and natural writing over GPT-5.6 Terra for complex workflows. In OpenAI's Agents' Last Exam evaluation, it scored 15 percentage points higher than GPT-5.6 Terra, performed on par with GPT-6 Astra, and completed tasks more than three times faster than GPT-6 Astra and GPT-6.1 Sol. GPT-6.1 Terra offers balanced performance with input and output token prices at least 50% lower than those of GPT-6.1 Sol and GPT-5.6 Terra. For more information about model development and performance, see the model/service card.

Input Modalities	Output Modalities
Audio	Embedding
Image	Image
Speech	Speech
Text	Text
Video	Video

Endpoints and APIs supported

The following tables show which endpoints and APIs are supported for GPT-6.1 Terra. For more information, see APIs supported by Amazon Bedrock and Inference endpoints supported by Amazon Bedrock.

Endpoint support

Endpoint	Supported
bedrock-runtime
bedrock-mantle

APIs supported on bedrock-runtime endpoint

Messages	Responses	Chat Completions	Converse	Invoke

APIs supported on bedrock-mantle endpoint

Messages	Responses	Chat Completions	Converse	Invoke

Capabilities and Features

Bedrock Features

Features supported using bedrock-runtime endpoint

Features supported using bedrock-mantle endpoint

JSON Schema output on bedrock-runtime

Use JSON Schema to set the format of the model response. These settings apply to non-streaming calls on the bedrock-runtime endpoint. Choose a profile ID from Programmatic access.

Use an object schema. Put all fields in required. Set additionalProperties to false. Validate your schema before you send it. Check for refusals or incomplete responses before you parse the output.

{
  "additionalModelRequestFields": {
    "text": {
      "format": { "strict": true }
    }
  }
}

Converse content blocks

On the bedrock-runtime endpoint, Converse accepts text and image content blocks in user messages. This model doesn't accept document content blocks, and it doesn't accept an image content block inside a toolResult. A request that includes either is rejected with a ValidationException. To send a document to this model, use the Responses API with an input_file content part instead.

Pricing

All prices are in USD per 1 million tokens for the Standard tier.

The Input — 30-minute cache write column is the price per 1 million tokens written to the prompt cache. The 30-minute figure is how long a cached prompt prefix is retained, not a quantity of tokens. For more information, see prompt caching.

Commercial In-Region prices include a 10% fee over OpenAI rates. You do not need to add this fee.

Priority and Flex tiers are not supported for this model.

Commercial Regions — short context (272K input tokens or fewer)

Commercial Regions — long context (more than 272K input tokens)

Programmatic Access

Use the following model IDs and endpoint URLs to access this model programmatically. For more information about the available APIs and endpoints, see APIs supported and Endpoints supported.

For example, if region is us-east-1 (N. Virginia), then the bedrock-mantle endpoint URL will be "https://bedrock-mantle.us-east-1.api.aws/openai/v1". On bedrock-runtime, the base URL is "https://bedrock-runtime.{region}.amazonaws.com/openai/v1" and requests must name the geographic cross-Region inference ID us.openai.gpt-6.1-terra as the model.

Service Tiers

Amazon Bedrock offers multiple service tiers to match your workload requirements. Standard provides pay-per-token access with no commitment (set "service_tier": "default" or omit the field). Priority delivers the fastest response times for a price premium (set "service_tier": "priority"). Flex provides lower-cost access for flexible, non-time-sensitive workloads (set "service_tier": "flex"). Reserved provides dedicated throughput with a term commitment for predictable workloads; it is set at the account level rather than per request (contact your AWS account team to enable). For more information, see service tiers.

Standard	Priority	Flex	Reserved

Regional Availability

Regional availability at a glance

Amazon Bedrock offers three inference options: In-Region keeps requests within a single Region for strict compliance, Geo Cross-Region routes across Regions within a geography (such as US, EU, and APAC) while respecting data residency, and Global Cross-Region routes anywhere worldwide when there are no residency constraints. Refer to the Regional availability by models page for more details.

Availability differs by endpoint.

Availability using the bedrock-mantle endpoint

Region	In-Region	Geo	Global
us-east-1 (N. Virginia)
us-east-2 (Ohio)
us-west-2 (Oregon)
us-gov-west-1 (GovCloud West)
us-gov-east-1 (GovCloud East)

Availability using the bedrock-runtime endpoint

Region	In-Region	Geo	Global
us-east-1 (N. Virginia)
us-east-2 (Ohio)
us-west-1 (N. California)
us-west-2 (Oregon)
ca-central-1 (Canada)
ca-west-1 (Calgary)
eu-central-1 (Frankfurt)
eu-central-2 (Zurich)
eu-north-1 (Stockholm)
eu-south-1 (Milan)
eu-south-2 (Spain)
eu-west-1 (Ireland)
eu-west-2 (London)
eu-west-3 (Paris)
ap-east-2 (Taipei)
ap-northeast-1 (Tokyo)
ap-northeast-2 (Seoul)
ap-northeast-3 (Osaka)
ap-south-1 (Mumbai)
ap-south-2 (Hyderabad)
ap-southeast-1 (Singapore)
ap-southeast-2 (Sydney)
ap-southeast-3 (Jakarta)
ap-southeast-4 (Melbourne)
ap-southeast-5 (Malaysia)
ap-southeast-6 (New Zealand)
ap-southeast-7 (Thailand)
il-central-1 (Tel Aviv)
mx-central-1
me-south-1 (Bahrain)
af-south-1 (Cape Town)
sa-east-1 (São Paulo)

Geo inference details

The destination Regions available to a geographic inference profile depend on the source Region. To retrieve the current routing configuration, call GetInferenceProfile from the source Region.

Geo: US

Geo inference ID: us.openai.gpt-6.1-terra

Quotas and Limits

Your AWS account has default quotas to maintain the performance of the service and to ensure appropriate usage of Amazon Bedrock. The default quotas assigned to an account might be updated depending on regional factors, payment history, fraudulent usage, and/or approval of a quota increase request. For more information, see Quotas for Amazon Bedrock documentation and see the limits for the model.

On Amazon Bedrock, each request can contain a maximum of 100 image inputs.

On the bedrock-runtime endpoint, limits are managed as tokens per minute (TPM) with a 10x burndown rate, where 1 output token consumes 10 tokens.

Sample Code

Step 1 - AWS Account: If you have an AWS account already, skip this step. If you are new to AWS, sign up for an AWS account.

Step 2 - API key: Go to the Amazon Bedrock console and generate a long-term API key.

Step 3 - Get the SDK: To use this getting started guide, you must have Python already installed. Then install the relevant software depending on the APIs you are using.

For both APIs, choose the OpenAI SDK tab.

Step 4 - Set environment variables

Configure your environment to use the API key for authentication.

Step 5 - Run your first inference request

Save the file as bedrock-first-request.py

bedrock-mantle

Use the settings from Step 4 - Set environment variables. Choose the bedrock-mantle tab. Send your request with the Responses API.

bedrock-runtime: OpenAI SDK

Use the settings from Step 4 - Set environment variables. Choose the bedrock-runtime tab. Send your request with the Responses API.