Stable Image Core 1.0

Stability AI — Stable Image Core 1.0

Model Details

Stable Image Core is Stability AI's fast, affordable text-to-image model for rapid, high-volume content generation. For more information about model development and performance, see the model/service card.

• Legacy period: at least 6 months

• Model lifecycle policy: Model lifecycle (For Models Launched Prior to Sept 7 2026)

• Model EOL date: N/A

• End User License Agreements and Terms of Use: View

• Model lifecycle: Active

Input Modalities	Output Modalities	APIs supported	Endpoints supported
Audio	Embedding	Responses	bedrock-runtime
Image	Image	Chat Completions	bedrock-mantle
Speech	Speech	Invoke
Text	Text	Converse
Video	Video

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
Abuse detection Guardrails	Intelligent prompt routing Response streaming Prompt optimization Count tokens Knowledge base Model evaluation Prompt management Flows Agents

Pricing

For pricing information, see the Amazon Bedrock Pricing page.

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-runtime	stability.stable-image-core-v1:1	https://bedrock-runtime.{region}.amazonaws.com	Not supported	Not supported

For example, if region is us-west-2 (Oregon), then the bedrock-runtime endpoint URL will be "https://bedrock-runtime.us-west-2.amazonaws.com".

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.

Region	In-Region	Geo	Global
us-west-2 (Oregon)

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.

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.

pip install boto3

Step 4 - Set environment variables: Configure your environment to use the API key for authentication.

AWS_BEARER_TOKEN_BEDROCK="<provide your Bedrock API key>"

Step 5 - Run your first inference request: Save the file as bedrock-first-request.py

Invoke API

import boto3
import json
import base64
import io
from PIL import Image

bedrock = boto3.client('bedrock-runtime', region_name='us-west-2')
response = bedrock.invoke_model(
    modelId='stability.stable-image-core-v1:1',
    body=json.dumps({
        'prompt': 'A car made out of vegetables.'
    })
)
output_body = json.loads(response["body"].read().decode("utf-8"))
base64_output_image = output_body["images"][0]
image_data = base64.b64decode(base64_output_image)
image = Image.open(io.BytesIO(image_data))
image.save("image.png")