I tried out OpenAI GPT-6 Astra, which became generally available on Amazon Bedrock

Introduction

On September 8, 2026, OpenAI's GPT-6 Astra became generally available on Amazon Bedrock.

https://aws.amazon.com/about-aws/whats-new/2026/09/openai-gpt-6-astra-on-amazon-bedrock/

I tried calling GPT-6 Astra via the Converse/OpenAI-compatible path of bedrock-runtime and the OpenAI-compatible path of bedrock-mantle.

Validation Details

Model List

I retrieved the list of foundation models filtered by the OpenAI provider.

aws bedrock list-foundation-models \
  --by-provider OpenAI \
  --region us-east-1

The returned list included the following entry.

{
    "modelArn": "arn:aws:bedrock:us-east-1::foundation-model/openai.gpt-6-astra",
    "modelId": "openai.gpt-6-astra",
    "modelName": "GPT-6 Astra",
    "providerName": "OpenAI",
    "inputModalities": [
        "TEXT",
        "IMAGE"
    ],
    "outputModalities": [
        "TEXT"
    ],
    "responseStreamingSupported": true,
    "customizationsSupported": [],
    "inferenceTypesSupported": [
        "INFERENCE_PROFILE"
    ],
    "modelLifecycle": {
        "status": "ACTIVE",
        "startOfLifeTime": "2026-09-08T17:00:00+00:00"
    }
}

The model ID is openai.gpt-6-astra, with text and image inputs and text output. Response streaming is also supported.

Converse

The model ID appearing in the list is openai.gpt-6-astra, but when calling it you specify global.openai.gpt-6-astra. I ran Converse against us-east-1.

aws bedrock-runtime converse \
  --region us-east-1 \
  --model-id global.openai.gpt-6-astra \
  --messages '[{"role":"user","content":[{"text":"Say ok"}]}]' \
  --inference-config '{"maxTokens":64}'

The response was as follows.

{
    "output": {
        "message": {
            "role": "assistant",
            "content": [
                {
                    "text": "ok"
                }
            ]
        }
    },
    "stopReason": "end_turn",
    "usage": {
        "inputTokens": 8,
        "outputTokens": 5,
        "totalTokens": 13,
        "cacheReadInputTokens": 0
    }
}

When I sent the same request to us-east-1, us-west-2, and ap-northeast-1, all returned stopReason end_turn, response text ok, and usage of input 8 / output 5 / total 13.

I also received a response using InvokeModel with the same model ID.

Here is the result of passing {"messages":[{"role":"user","content":"Say ok"}],"max_completion_tokens":16} as the input body.

{"choices":[{"finish_reason":"stop","index":0,"message":{"annotations":[],"content":"ok","refusal":null,"role":"assistant"}}],"created":1788919066,"id":"chatcmpl-y3ldv7w2nvet7ibewtphemha2kmyudqvrasturqhjottjo2tbm3q","model":"global.openai.gpt-6-astra","object":"chat.completion","service_tier":"default","usage":{"completion_tokens":5,"completion_tokens_details":{"accepted_prediction_tokens":0,"audio_tokens":0,"reasoning_tokens":0,"rejected_prediction_tokens":0},"prompt_tokens":8,"prompt_tokens_details":{"audio_tokens":0,"cache_write_tokens":0,"cached_tokens":0},"total_tokens":13},"system_fingerprint":null}

OpenAI-Compatible Path

I sent a Responses API format body to the OpenAI-compatible path of bedrock-runtime.

{"model":"global.openai.gpt-6-astra","input":"Say ok","store":false}

I saved this as gpt6-astra-responses-body.json and sent it to /openai/v1/responses using curl. For authentication, I passed temporary credentials from environment variables—the same ones used by the AWS CLI—and added SigV4 signing to curl.

curl --silent --show-error \
  --aws-sigv4 'aws:amz:us-east-1:bedrock' \
  --user "$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY" \
  -H "x-amz-security-token: $AWS_SESSION_TOKEN" \
  -H 'Content-Type: application/json' \
  --data-binary @gpt6-astra-responses-body.json \
  https://bedrock-runtime.us-east-1.amazonaws.com/openai/v1/responses

The HTTP status was 200. The response model was global.openai.gpt-6-astra, status was completed, the output text was ok, and usage was the same as Converse: 8 / 5 / 13 (input_tokens / output_tokens / total_tokens).

When sending to /openai/v1/chat/completions with the same model ID, the HTTP status was also 200. The finish_reason was stop, the response text was ok, and usage was prompt_tokens 8 / completion_tokens 5 / total_tokens 13. The output limit was specified using max_completion_tokens.

import json
import os

import httpx
from botocore.auth import SigV4Auth
from botocore.awsrequest import AWSRequest
from botocore.credentials import Credentials
from openai import OpenAI

REGION = "us-west-2"
SIGNING_SERVICE = "bedrock"
MODEL_ID = "global.openai.gpt-6-astra"

credentials = Credentials(
    os.environ["AWS_ACCESS_KEY_ID"],
    os.environ["AWS_SECRET_ACCESS_KEY"],
    os.environ.get("AWS_SESSION_TOKEN"),
)

class SigV4Signer(httpx.Auth):
    requires_request_body = True

    def auth_flow(self, request):
        aws_request = AWSRequest(
            method=request.method,
            url=str(request.url),
            data=request.content,
            headers={"content-type": request.headers.get("content-type", "application/json")},
        )
        SigV4Auth(credentials, SIGNING_SERVICE, REGION).add_auth(aws_request)
        for key, value in aws_request.headers.items():
            request.headers[key] = value
        yield request

client = OpenAI(
    api_key="unused-sigv4",
    base_url=f"https://bedrock-runtime.{REGION}.amazonaws.com/openai/v1",
    http_client=httpx.Client(auth=SigV4Signer(), timeout=120.0),
)

response = client.responses.create(
    model=MODEL_ID,
    input="Can you explain the features of Amazon Bedrock?",
)
print("=== output_text ===")
print(response.output_text)
print("=== usage ===")
print(json.dumps(response.usage.model_dump(), indent=2))

=== output_text ===
**Amazon Bedrock is AWS's managed platform for building generative AI applications.** It gives you access to foundation models and tools for connecting them to your data, automating workflows, and adding security controls—without having to manage the underlying model-serving infrastructure for its standard managed offerings.

Here are its main features:

### 1. Access to multiple foundation models
...

=== usage ===
{
  "input_tokens": 16,
  "output_tokens": 1010,
  "total_tokens": 1026
}

Mantle

The official documentation recommends bedrock-runtime for new applications, but GPT-6 Astra was also available from bedrock-mantle. When I sent openai.gpt-6-astra to the OpenAI-compatible Responses API in us-west-2, it returned HTTP 200 with status: completed. The output text was ok, and usage was also the same 8 / 5 / 13.

Unlike bedrock-runtime, the model ID specified for bedrock-mantle does not include a profile prefix. When I specified the same model ID in us-east-1, I received an HTTP 404 with The model 'openai.gpt-6-astra' does not exist. The model card also states that bedrock-mantle availability is limited to us-west-2 only.

REGION = "us-west-2"
SIGNING_SERVICE = "bedrock-mantle"
MODEL_ID = "openai.gpt-6-astra"

client = OpenAI(
    api_key="unused-sigv4",
    base_url=f"https://bedrock-mantle.{REGION}.api.aws/openai/v1",
    http_client=httpx.Client(auth=SigV4Signer(), timeout=120.0),
)

response = client.responses.create(
    model=MODEL_ID,
    input="Can you explain the features of Amazon Bedrock?",
)
print(response.output_text)

**Amazon Bedrock is AWS's fully managed service for building generative AI applications.** It lets you access foundation models, connect them to your data, and deploy AI-powered workflows without managing the underlying model-serving infrastructure.

Its main features include:

### 1. Access to multiple foundation models
...

Pricing

Bedrock's unit pricing is listed on the model card, and direct pricing is on the OpenAI model page. The table below shows the list price per 1M tokens for short contexts up to 272K tokens.

Summary

I was able to call GPT-6 Astra immediately after GA from both Bedrock's native Converse and the OpenAI-compatible Responses API.

I look forward to seeing GPT-6 Astra, which is now available on Amazon Bedrock, deployed to AI agents such as Kiro, and to geography-based cross-region inference expanding to regions beyond North America.