o1-all

Technical Specifications of o1-all

What is o1-all?

o1-all is CometAPI’s platform identifier for accessing an OpenAI O1-series reasoning model experience through a unified API layer. CometAPI explicitly presents it as a way to try the full O1 series, alongside o1-pro-all, rather than as a separate foundational model family of its own.

In practical terms, this means o1-all is intended for workloads where stronger reasoning matters more than raw low-latency text generation: multi-step analysis, difficult classification, answer verification, evaluation pipelines, and tasks where the model must interpret context carefully. That aligns with OpenAI’s positioning of o1 as a full o-series reasoning model and with its documented best practices for reasoning-heavy applications.

For developers using CometAPI, the advantage is operational simplicity: you call a single OpenAI-compatible API surface, authenticate once, and specify o1-all as the model ID in your request body. CometAPI handles the aggregation layer while preserving a familiar integration pattern.

Main features of o1-all

• Reasoning-oriented behavior: o1-all is designed for use cases that benefit from deeper deliberation, such as analytical prompts, validation, and hard decision logic, consistent with OpenAI’s documented o1 reasoning use cases.
• Unified O1-series access through CometAPI: CometAPI describes o1-all as an easy way to experience the full O1 series from its aggregated platform.
• OpenAI-compatible integration: Existing OpenAI-style clients can be adapted by changing the base URL and model name, reducing migration effort for teams already using chat-completions style APIs.
• Commercial usability: The CometAPI model page explicitly marks the model as available for commercial use.
• Simple pricing model: Instead of token-tier complexity on the model page itself, CometAPI lists o1-all with per-request pricing, making budgeting straightforward for some application types.
• Prompting best-practice fit: Reasoning models like o1 generally perform best with concise, direct instructions rather than “think step by step” prompting, which can improve reliability in production prompt design.

How to access and integrate o1-all

Step 1: Sign Up for API Key

Create a CometAPI account and generate your API key from the dashboard. CometAPI issues sk-... style credentials and exposes an OpenAI-compatible API surface, so one key can be used across integrated models, including o1-all. Configure your client with a CometAPI base URL such as https://api.cometapi.com/v1.

Step 2: Send Requests to o1-all API

Use the standard OpenAI-compatible chat completions pattern and set the model field to o1-all.

curl https://api.cometapi.com/v1/chat/completions \
  -H "Authorization: Bearer $COMETAPI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "o1-all",
    "messages": [
      {
        "role": "user",
        "content": "Solve this step-by-step: A store gives 15% off a $120 item, then applies 8% tax. What is the final price?"
      }
    ]
  }'

Python example:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_COMETAPI_KEY",
    base_url="https://api.cometapi.com/v1"
)

response = client.chat.completions.create(
    model="o1-all",
    messages=[
        {"role": "user", "content": "Analyze the pros and cons of using retrieval-augmented generation for enterprise search."}
    ]
)

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

This request pattern follows CometAPI’s documented OpenAI-style integration approach, with the main customization being the model ID o1-all.

Step 3: Retrieve and Verify Results

After the API returns a response, extract the generated content from the first choice and validate it against your application’s requirements. For reasoning-heavy workloads, verification is especially important: OpenAI’s guidance notes that o1-class models are often used for evaluation, grading, and validation because they can detect nuanced differences better than general-purpose models in difficult tasks.

In production, common verification steps include checking output format, comparing answers against known constraints, running secondary validations for numeric or policy-sensitive tasks, and logging failures for iterative prompt refinement. If a request fails, CometAPI documents standard API error patterns such as 400, 401, 404, 429, 500, and 503 for troubleshooting.