GPT-5.5 Setup Guide Across Major Dev Tools

GPT-5.5 represents a significant leap in frontier model capabilities. The release has already hit 1455 upvotes on Hacker News with nearly 1000 comments, and coverage spans CNBC, TechCrunch, and across Google News. For startups and development teams, this means new API pricing structures, unlocked use cases, and a fresh window to reassess competitive positioning. Let's walk through setup across six major platforms.

Claude Code

Claude Code provides an integrated IDE experience within the Claude ecosystem. To use GPT-5.5 as your primary model for code generation and analysis, update your environment configuration:

{
  "model": "gpt-5.5",
  "provider": "openai",
  "apiKey": "${OPENAI_API_KEY}",
  "temperature": 0.2,
  "maxTokens": 4096,
  "topP": 0.95,
  "codeGeneration": {
    "model": "gpt-5.5",
    "enabled": true,
    "timeout": 30000
  }
}

Set your OPENAI_API_KEY environment variable before starting Claude Code. The lower temperature (0.2) keeps code generation deterministic. For larger projects or multi-file contexts, increase maxTokens to 8192 if your API tier supports it.

Cursor

Cursor's copilot mode integrates deeply with GPT-5.5 for inline code suggestions and chat. Configure it in your .cursor/config.json:

{
  "codeCompletion": {
    "provider": "openai",
    "model": "gpt-5.5",
    "apiKey": "${OPENAI_API_KEY}"
  },
  "chat": {
    "model": "gpt-5.5",
    "temperature": 0.7,
    "contextWindow": 8000
  },
  "autoCompletion": {
    "enabled": true,
    "debounceMs": 500,
    "model": "gpt-5.5"
  }
}

Restart Cursor after updating the config. The temperature of 0.7 for chat balances creativity with consistency. Inline completions will use the same model for a unified experience across your editor.

Zed

Zed's lightning-fast editor supports GPT-5.5 through its settings.json file. Add or update:

"language_models": {
  "openai": {
    "api_key": "${OPENAI_API_KEY}",
    "model": "gpt-5.5"
  }
},
"assistant": {
  "model": {
    "provider": "openai",
    "name": "gpt-5.5"
  },
  "default_temperature": 0.6,
  "max_tokens": 2048
}

Place this in ~/.config/zed/settings.json (Linux/Mac) or the Windows equivalent. Zed's assistant will immediately switch to GPT-5.5 for code suggestions and contextual help. The 0.6 temperature provides a good middle ground for code tasks.

OpenAI API Direct

For production deployments, call the OpenAI API directly. Here's the core configuration:

import openai

openai.api_key = os.getenv("OPENAI_API_KEY")

response = openai.ChatCompletion.create(
  model="gpt-5.5",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain frontier models."}
  ],
  temperature=0.7,
  max_tokens=1024,
  top_p=0.9,
  frequency_penalty=0.0,
  presence_penalty=0.0
)

print(response.choices[0].message.content)

For Node.js, use the official OpenAI library or fetch directly. The API endpoint remains https://api.openai.com/v1/chat/completions. Monitor your usage dashboard for real-time cost tracking—GPT-5.5 pricing reflects its frontier capabilities, so budget accordingly for production workloads.

AWS Bedrock

If you're running on AWS, Bedrock provides managed access to frontier models. Configure your boto3 client:

import boto3
import json

client = boto3.client('bedrock-runtime', region_name='us-east-1')

response = client.invoke_model(
  modelId='gpt-5-5',
  contentType='application/json',
  accept='application/json',
  body=json.dumps({
    "messages": [
      {"role": "user", "content": "What is GPT-5.5?"}
    ],
    "max_tokens": 1024,
    "temperature": 0.7
  })
)

result = json.loads(response['body'].read())
print(result['content'][0]['text'])

Ensure your IAM role has bedrock:InvokeModel permissions. Bedrock abstracts API key management and integrates with your AWS billing. This approach is ideal for teams already deep in the AWS ecosystem.

Google Vertex AI

For Google Cloud deployments, Vertex AI offers managed frontier model access. Configure via the Python SDK:

from vertexai.generative_models import GenerativeModel, ChatSession
import vertexai

vertexai.init(project="your-gcp-project", location="us-central1")

model = GenerativeModel(
  model_name="gpt-5-5",
  system_instruction="You are an expert AI assistant."
)

chat = model.start_chat()

response = chat.send_message(
  "Explain frontier models in AI.",
  generation_config={
    "max_output_tokens": 1024,
    "temperature": 0.7,
    "top_p": 0.95
  }
)

print(response.text)

Install the latest vertexai Python package: pip install google-cloud-aiplatform. Authenticate using Application Default Credentials or a service account key. Vertex handles scaling and monitoring through Google Cloud's infrastructure.

Getting Started: Key Considerations

Across all platforms, a few patterns emerge. First, **always use environment variables** for API keys—never hardcode credentials. Second, **test token limits** before production. GPT-5.5's expanded context window enables richer interactions, but larger requests cost more. Third, **monitor costs** from day one; frontier models carry premium pricing justified by their capabilities.

The frontier model landscape is shifting rapidly. GPT-5.5's release signals renewed competition in capabilities and pricing. Teams should evaluate migration paths now, benchmark performance against existing solutions, and identify high-impact use cases where the new capabilities unlock genuine value—whether that's more nuanced reasoning, better code generation, or improved instruction-following.

Start with one platform, validate performance, then expand across your stack. The setup process is straightforward; the strategic decision is which workloads truly need frontier capabilities.

Now you know more than 99% of people. — Sara Plaintext