Technical Specifications of o1-mini-2024-09-12
| Specification | Details |
|---|---|
| Model ID | o1-mini-2024-09-12 |
| Provider | OpenAI |
| Model family | o1 series reasoning model |
| Release snapshot | September 12, 2024 snapshot of o1-mini |
| Primary modality | Text input, text output |
| Core strength | Cost-efficient reasoning, especially for STEM, math, and coding tasks |
| Relative positioning | Faster and lower-cost than o1-preview, with strong performance on coding-oriented reasoning workloads |
| Training approach | Reinforcement-learning-based reasoning model designed to spend more time thinking before responding |
| Availability status | Snapshot listed by OpenAI as deprecated |
What is o1-mini-2024-09-12?
o1-mini-2024-09-12 is a snapshot of OpenAI’s o1-mini reasoning model, released on September 12, 2024. It belongs to the o1 family, which OpenAI introduced as models that “think before they answer” and are optimized for complex reasoning rather than only fast next-token generation.
Compared with larger o1 variants, o1-mini was positioned as the faster and more economical option for workloads that need strong reasoning without requiring the broadest possible world knowledge. OpenAI specifically highlighted its usefulness for STEM-heavy applications, noting that it performs especially well in math and coding and was designed as a cost-efficient alternative to o1-preview.
In practical terms, this makes o1-mini-2024-09-12 a good fit for developers building applications such as code assistants, technical problem-solving tools, structured analytical workflows, and math-focused copilots. Because this exact snapshot is now marked deprecated in OpenAI’s model documentation, teams using the CometAPI identifier should verify ongoing compatibility and behavior in their own environment.
Main features of o1-mini-2024-09-12
- Reasoning-first design: OpenAI describes the o1 series as models trained to perform complex reasoning and to spend more time thinking before responding, which can improve performance on multi-step technical tasks.
- Strong STEM performance: o1-mini was explicitly introduced as excelling at STEM workloads, especially math and coding, making it suitable for engineering and analytical use cases.
- Lower cost profile: OpenAI stated that o1-mini launched at a significantly lower cost than o1-preview, positioning it as the more budget-friendly reasoning option.
- Faster response characteristics: The model was presented as faster than o1-preview, which is useful when balancing reasoning quality with latency-sensitive application needs.
- Good fit for coding applications: OpenAI’s release materials and system documentation repeatedly describe o1-mini as particularly effective for coding-related tasks.
- Snapshot stability: Using the exact snapshot ID
o1-mini-2024-09-12can help teams target a fixed model version for reproducibility, though OpenAI currently labels this snapshot as deprecated.
How to access and integrate o1-mini-2024-09-12
Step 1: Sign Up for API Key
To access o1-mini-2024-09-12, first create an account on CometAPI and generate an API key from the dashboard. Once you have your key, store it securely as an environment variable so your application can authenticate requests to the API.
Step 2: Send Requests to o1-mini-2024-09-12 API
After getting your API key, send requests to CometAPI’s OpenAI-compatible endpoint while setting the model field to o1-mini-2024-09-12.
curl https://api.cometapi.com/v1/responses \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_API_KEY" \
-d '{
"model": "o1-mini-2024-09-12",
"input": "Write a Python function that solves a quadratic equation and explain the math."
}'
You can also use the OpenAI SDK format by pointing the client to CometAPI’s base URL and keeping the model name as o1-mini-2024-09-12.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_COMETAPI_KEY",
base_url="https://api.cometapi.com/v1"
)
response = client.responses.create(
model="o1-mini-2024-09-12",
input="Solve this step by step: If 3x + 5 = 20, what is x?"
)
print(response)
Step 3: Retrieve and Verify Results
Once the API returns a response, parse the output text in your application and validate it for your use case. For reasoning-heavy tasks such as coding, mathematics, or technical analysis, it is a good practice to add automated checks, test cases, or human review to verify that the model’s conclusions are correct before using them in production.