GPT-5.6 Luna
OpenAI — GPT-5.6 Luna
Model Details
GPT-5.6 Luna is the fast and affordable model from OpenAI. Use Luna for high-volume inference tasks like classification, summarization, routing, and real-time applications where latency and cost per token matter most. For more information about model development and performance, see the model/service card
Model launch date: July 13, 2026
Model EOL date: N/A
End User License Agreements and Terms of Use: View
Model lifecycle: Active
Context window: 1M tokens
| 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-5.6 Luna. For more information, see APIs supported by Amazon Bedrock and 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 |
|---|---|---|---|---|
|
|
|
|
|
On bedrock-mantle, this model is served at /openai/v1/responses, not the default /v1/responses.
Tip
Whenever possible, we recommend using the bedrock-runtime endpoint for new applications. See Endpoints supported by Amazon Bedrock for details.
Capabilities and Features
Bedrock Features
Features supported using bedrock-runtime endpoint
| Supported | Not Supported |
|---|---|
|
Features supported using bedrock-mantle endpoint
| Supported | Not Supported |
|---|---|
|
— |
Pricing
Short Context Window (272K)
| Inference option | Input | Input — 30m cache write | Input — cache read | Output |
|---|---|---|---|---|
| In-Region | $0.22 | $0.275 | $0.022 | $1.32 |
| Geo CRIS | $0.22 | $0.275 | $0.022 | $1.32 |
| Global CRIS | $0.20 | $0.25 | $0.02 | $1.20 |
Long Context Window (1M)
| Inference option | Input | Input — 30m cache write | Input — cache read | Output |
|---|---|---|---|---|
| In-Region | $0.44 | $0.55 | $0.044 | $1.98 |
| Geo CRIS | $0.44 | $0.55 | $0.044 | $1.98 |
| Global CRIS | $0.40 | $0.50 | $0.04 | $1.80 |
All prices are per 1 million tokens. Pricing shown is for the Standard tier. Priority and Flex tiers are not supported for this model.
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.
| Endpoint | Model ID | In-Region endpoint URL | Geo inference ID | Global inference ID |
|---|---|---|---|---|
bedrock-mantle |
openai.gpt-5.6-luna |
https://bedrock-mantle.{region}.api.aws/openai/v1 |
Not supported | Not supported |
bedrock-runtime |
openai.gpt-5.6-luna |
Not supported | us.openai.gpt-5.6-luna in the commercial AWS Regions, in.openai.gpt-5.6-luna in the India Regions |
global.openai.gpt-5.6-luna |
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-5.6-luna 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) | | | |
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) | | | |
me-central-1 (UAE) | | | |
me-south-1 (Bahrain) | | | |
af-south-1 (Cape Town) | | | |
sa-east-1 (São Paulo) | | | |
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 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
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.
Step 4 - Set environment variables: Configure your environment to use the API key for authentication.
Note
On bedrock-runtime, name a cross-Region inference profile as the model — us.openai.gpt-5.6-luna or global.openai.gpt-5.6-luna. This model is not available for in-Region inference on that endpoint. Your IAM identity also needs bedrock:InvokeModel on your account's default project (arn:aws:bedrock:{region}:{account-id}:project/default) in addition to the inference profile.
Step 5 - Run your first inference request: Save the file as bedrock-first-request.py
