Technical Specifications of o1-pro-all
| Attribute | Details |
|---|---|
| Model ID | o1-pro-all |
| Provider family | OpenAI o1 series |
| Model type | Reasoning-focused large language model |
| Core positioning | Higher-compute version of o1 for more reliable answers on difficult tasks |
| Input modalities | Text, image |
| Output modalities | Text |
| Context window | 200,000 tokens |
| Max output | 100,000 tokens |
| Knowledge cutoff | October 1, 2023 |
| API compatibility | Best aligned with the Responses API |
| Reasoning support | Yes |
| Structured outputs | Supported |
| Function calling | Supported |
| Streaming | Not supported |
| Fine-tuning | Not supported |
| Distillation | Not supported |
| Predicted outputs | Not supported |
What is o1-pro-all?
o1-pro-all is CometAPI’s platform identifier for access to OpenAI’s o1-pro reasoning model. Based on OpenAI’s model documentation, o1-pro is a more compute-intensive version of o1 that is designed to “think harder” before responding, making it suitable for complex reasoning, advanced coding, math-heavy workflows, and high-stakes analytical tasks.
Compared with standard general-purpose models, this model is positioned for accuracy and depth rather than speed. It is slower and significantly more expensive than baseline o1-tier models, but it is intended to deliver more consistent performance on difficult prompts, multi-step problem solving, and expert-oriented workloads. Through CometAPI, o1-pro-all lets developers call this capability using a unified model ID without integrating directly against a single upstream vendor configuration.
Main features
- Advanced reasoning depth:
o1-pro-allis built for tasks that benefit from extra inference-time compute, such as multi-step analysis, formal reasoning, difficult debugging, and technical problem solving. - Large context handling: With a 200K-token context window, it can work across long documents, extensive instructions, large codebases, and multi-part conversations.
- High output ceiling: The model supports up to 100K output tokens, which is useful for long-form reports, detailed explanations, and substantial generated artifacts.
- Text-and-image input support: It can accept both text and image inputs, enabling workflows where visual material must be analyzed alongside written instructions.
- Structured integration options: Support for function calling and structured outputs makes it easier to connect the model to production applications, internal tools, and downstream parsers.
- Reasoning-first tradeoff: This model prioritizes answer quality and consistency over latency, so it is best used when correctness matters more than response speed.
- Enterprise-friendly aggregation: Through CometAPI,
o1-pro-allcan be accessed with a unified API layer, simplifying vendor abstraction, routing, and deployment across applications.
How to access and integrate
Step 1: Sign Up for API Key
Sign up on CometAPI and generate your API key from the dashboard. After that, store the key securely as an environment variable so your application can authenticate requests safely.
Step 2: Send Requests to o1-pro-all API
Use CometAPI’s OpenAI-compatible endpoint and set the model field to o1-pro-all.
curl https://api.cometapi.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_API_KEY" \
-d '{
"model": "o1-pro-all",
"messages": [
{
"role": "user",
"content": "Analyze this architecture decision and recommend the best option."
}
]
}'
from openai import OpenAI
client = OpenAI(
api_key="YOUR_COMETAPI_API_KEY",
base_url="https://api.cometapi.com/v1"
)
response = client.chat.completions.create(
model="o1-pro-all",
messages=[
{"role": "user", "content": "Analyze this architecture decision and recommend the best option."}
]
)
print(response.choices[0].message.content)
Step 3: Retrieve and Verify Results
Parse the response content from the API result, then validate output quality against your task requirements. For production use, add retries, schema validation, and human review for sensitive reasoning workflows to ensure the model’s conclusions are reliable.