awsai mlPractitioner

Unlocking AI Development with OpenAI GPT-5.5 and Codex on Amazon Bedrock

5 min read AWS BlogJun 1, 2026Reviewed for accuracy

Practitioner — Hands-on experience recommended

OpenAI's GPT-5.5 and Codex models on Amazon Bedrock offer a game-changing approach to AI-powered software development. By integrating these models, you can enhance your applications with advanced natural language processing and coding capabilities, addressing complex tasks that require intelligent reasoning and code generation.

You can access these models programmatically using the OpenAI Responses API, which is built on Bedrock's next-generation inference engine. This allows you to call the bedrock-mantle endpoints through the OpenAI SDK or command-line tools like curl. Key configuration parameters include the OPENAI_BASE_URL, which defaults to 'https://bedrock-mantle.us-east-2.api.aws/openai/v1', and the OPENAI_API_KEY for authentication. For instance, you can set the model ID to 'openai.gpt-5.5' to utilize the latest capabilities in your requests.

In production, you need to be aware of model latency, especially when using GPT-5.5 and GPT-5.4. The perceived latency can vary based on several factors, including reasoning effort and output length. Start with medium effort for GPT-5.5 and explicitly set effort for GPT-5.4 to avoid unexpected delays. Additionally, during high demand periods, requests may be queued rather than rejected, so plan your capacity accordingly to ensure smooth operations.

Key takeaways

→Access models using the OpenAI Responses API for high performance.
→Set OPENAI_BASE_URL to 'https://bedrock-mantle.us-east-2.api.aws/openai/v1' for API calls.
→Use 'openai.gpt-5.5' as the model ID for the latest features.
→Monitor model latency and start with medium effort for GPT-5.5.
→Prepare for request queuing during high demand to maintain service reliability.

Why it matters

Integrating GPT-5.5 and Codex can significantly enhance your software development processes, enabling faster and more efficient coding solutions that adapt to complex requirements in real-time.

Code examples

Python

1import os
2from openai import OpenAI
3
4client = OpenAI(
5    base_url=os.environ["OPENAI_BASE_URL"],
6    api_key=os.environ["OPENAI_API_KEY"],
7)
8
9response = client.responses.create(
10    model=os.environ["BEDROCK_OPENAI_MODEL_ID"],
11    input=[
12        {
13            "role": "developer",
14            "content": "You are a software engineer with excellent AWS cloud knowledge. Be concise and practical.",
15        },
16        {
17            "role": "user",
18            "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions.",
19        },
20    ],
21    reasoning={"effort": "medium"},
22    text={"verbosity": "low"},
23)
24
25print(response.output_text)

Shell

1curl "$OPENAI_BASE_URL/responses" \
2  -H "Content-Type: application/json" \
3  -H "Authorization: Bearer $OPENAI_API_KEY" \
4  -d '{
5    "model": "openai.gpt-5.5",
6    "input": [
7      {
8        "role": "developer",
9        "content": "You are a software engineer with excellent AWS cloud knowledge."
10      },
11      {
12        "role": "user",
13        "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions."
14      }
15    ],
16    "reasoning": {"effort": "medium"},
17    "text": {"verbosity": "low"}
18  }'