Technical Specifications of o1-2024-12-17
| Specification | Details |
|---|---|
| Model ID | o1-2024-12-17 |
| Provider / family | OpenAI o1 reasoning model family. |
| Model type | Frontier reasoning large language model optimized for complex problem-solving, coding, math, science, and multi-step analysis. |
| Release snapshot | o1-2024-12-17 is the dated snapshot released by OpenAI in December 2024. |
| Input modalities | Text and image input. |
| Output modalities | Text output. |
| Context window | 200K tokens. |
| Max output | Up to 100K output tokens per request. |
| Performance profile | Slower than lighter models, but designed for deeper reasoning and higher-quality answers on hard tasks. |
| Reasoning controls | Supports reasoning_effort so developers can tune how long the model thinks before answering. |
| Prompting behavior | With o1 models and newer, developer messages replace older system-message style guidance; starting with o1-2024-12-17, markdown is suppressed by default unless explicitly re-enabled in the developer message. |
What is o1-2024-12-17?
o1-2024-12-17 is CometAPI’s platform identifier for OpenAI’s December 17, 2024 snapshot of the o1 reasoning model. It belongs to the o1 series, which OpenAI describes as models trained with reinforcement learning to perform complex reasoning and to “think before they answer.”
Compared with conventional chat-oriented models, o1-2024-12-17 is aimed at tasks where correctness, multi-step logic, and careful analysis matter more than raw speed. OpenAI positions o1 as a full reasoning model for advanced use cases, with support for text and image inputs and text outputs.
This particular snapshot was introduced as an updated post-trained version of o1, improving model behavior based on feedback while preserving the frontier-level reasoning capabilities evaluated for the o1 family. OpenAI also reported lower latency relative to o1-preview, using on average 60% fewer reasoning tokens for a given request.
Main features of o1-2024-12-17
- Advanced reasoning: Built for hard problems that require step-by-step thinking, including mathematics, science, logic, and difficult coding workflows.
- Vision input support: Can reason over image inputs in addition to text, which is useful for visual analysis, diagrams, scientific workflows, and technical problem-solving.
- Long context handling: Supports a 200K-token context window, making it suitable for large documents, long conversations, and multi-file reasoning tasks.
- Large response capacity: Can generate up to 100K output tokens in a single request, which helps for detailed reports, long-form reasoning, or substantial code generation.
- Adjustable reasoning depth: The
reasoning_effortparameter lets developers trade off latency and depth of reasoning based on the needs of the application. - Improved efficiency vs. preview: OpenAI states that
o1uses on average 60% fewer reasoning tokens thano1-previewfor a given request, improving practical efficiency. - Developer-message-first prompting: For
o1models and newer, developer messages are the preferred mechanism for high-level behavioral instructions, replacing the older system-message pattern. - Default plain-text behavior: Starting with
o1-2024-12-17, API responses avoid markdown formatting by default unless you explicitly re-enable it in the developer message.
How to access and integrate o1-2024-12-17
Step 1: Sign Up for API Key
To use o1-2024-12-17, first create an account on CometAPI and generate your API key from the dashboard. After that, store the key securely as an environment variable in your application so you can authenticate requests without hard-coding secrets in source files.
Step 2: Send Requests to o1-2024-12-17 API
Once your API key is ready, send requests through CometAPI’s OpenAI-compatible endpoint and set the model field to o1-2024-12-17.
curl https://api.cometapi.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_API_KEY" \
-d '{
"model": "o1-2024-12-17",
"messages": [
{
"role": "developer",
"content": "You are a precise reasoning assistant. Formatting re-enabled."
},
{
"role": "user",
"content": "Analyze the trade-offs between recursive descent and Pratt parsers."
}
]
}'
You can also integrate it from any OpenAI-compatible SDK by replacing the base URL with CometAPI’s endpoint and keeping o1-2024-12-17 as the target model ID.
Step 3: Retrieve and Verify Results
After submitting the request, parse the response JSON and read the generated assistant output from the returned choices or message content fields, depending on the SDK or endpoint you use. For production use, you should also verify outputs with application-level checks such as schema validation, test cases, citation workflows, or human review when correctness is critical.