o3-mini

Technical Specifications of o3-mini

What is o3-mini?

o3-mini is an artificial intelligence model provided by OpenAI. It is designed for developers who need a capable model for reasoning-heavy and general text-based workloads, including question answering, programming help, summarization, classification, content drafting, and workflow automation.

On CometAPI, o3-mini is referenced by the exact model ID o3-mini, which is the identifier you should use in requests when routing through the platform.

Main features of o3-mini

• Reasoning capability: Suitable for tasks that require multi-step thinking, analysis, and problem solving.
• Developer-friendly integration: Can be called through standard API patterns, making it straightforward to add to applications and backend workflows.
• Versatile text tasks: Useful for chat, transformation, extraction, summarization, coding support, and structured generation.
• Automation ready: Fits well in business processes, agents, internal tools, and productivity systems that need reliable AI text output.
• CometAPI compatibility: Accessible through CometAPI with the platform model identifier o3-mini.

How to access and integrate o3-mini

Step 1: Sign Up for API Key

To get started, create an account on CometAPI and generate your API key from the dashboard. This key is required to authenticate all requests. Once you have your key, store it securely and avoid exposing it in client-side code or public repositories.

Step 2: Send Requests to o3-mini API

After getting your API key, send requests to the CometAPI chat completions endpoint and set the model field to o3-mini.

curl https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_API_KEY" \
  -d '{
    "model": "o3-mini",
    "messages": [
      {
        "role": "user",
        "content": "Explain the benefits of using o3-mini for structured reasoning tasks."
      }
    ]
  }'

Step 3: Retrieve and Verify Results

After submission, the API returns a structured response containing the model output. Parse the response content from the returned choices, validate it against your application requirements, and add logging, retries, or output checks as needed for production integrations.