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const { OpenAI } = require('openai');
const api = new OpenAI({
baseURL: 'https://api.ai.cc/v1',
apiKey: '',
});
const main = async () => {
const result = await api.chat.completions.create({
model: 'o3-mini',
messages: [
{
role: 'user',
content: 'Tell me, why is the sky blue?'
}
],
});
const message = result.choices[0].message.content;
console.log(`Assistant: ${message}`);
};
main();
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
response = client.chat.completions.create(
model="o3-mini",
messages=[
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
-
AI Playground
Test all API models in the sandbox environment before you integrate.
We provide more than 300 models to integrate into your app.
Product Detail
✨ Introducing OpenAI o3-mini: A Compact Powerhouse LLM
OpenAI o3-mini is a sophisticated, yet efficient, large language model (LLM) designed to elevate reasoning capabilities. As a pivotal part of the o3 model series, it excels in tasks demanding structured reasoning and strict adherence to prompts. This makes it an ideal choice for developers, researchers, and educators working across various logical challenges, including programming and advanced mathematics.
Key Details:
- Model Name: OpenAI o3-mini
- Developer: OpenAI
- Release Date: January 30, 2025
- Model Type: Large Language Model (LLM)
🚀 Core Capabilities & Features
- Dense Transformer Architecture: This model employs an architecture where all parameters are engaged for every input token. This design ensures consistent and robust performance across a wide array of tasks.
- Extended Context Length: OpenAI o3-mini boasts an impressive capacity to process up to 200,000 tokens, allowing for extensive context retention throughout interactions and complex problem-solving.
- Deliberative Alignment Safety: A novel safety mechanism is integrated, designed to meticulously analyze prompts against predefined safety specifications. This helps in identifying and mitigating harmful content more effectively.
- High Benchmark Performance: The model demonstrates competitive results on various benchmarks, including an ARC-AGI score of 32% and an AIME 2024 score of 83.3%, underscoring its advanced reasoning abilities.
- Open Source Availability: Released under an MIT license, o3-mini encourages broad usage, modification, and community contribution.
💡 Intended Use & Language Support
OpenAI o3-mini is specifically tailored for individuals and organizations requiring advanced reasoning capabilities in their applications. It's particularly valuable for:
- Coding Assistance: Generating, debugging, and understanding code.
- Mathematical Problem-Solving: Tackling complex equations and logical puzzles.
- Logical Reasoning: Enhancing decision-making and pattern recognition.
While the model primarily supports English, it is adaptable to accommodate multiple languages based on specific user requirements.
⚙️ Technical Specifications
Architecture:
OpenAI o3-mini leverages a dense transformer architecture, meticulously optimized for superior reasoning tasks.
- Total Parameters: Estimated around 200 billion.
- Attention Mechanisms: All parameters are engaged per token processed, ensuring robust and consistent performance.
- Training Methodology: Utilizes reinforcement learning techniques to fine-tune and optimize its reasoning prowess.
Training Data:
The model was trained on an expansive and diverse dataset, encompassing a wide range of text sources.
- Data Source & Size: Over 14 trillion tokens from publicly available texts, including books, articles, and extensive code repositories.
- Knowledge Cutoff: The model's knowledge base is current as of January 2025.
- Diversity & Bias: Data curation prioritized minimizing biases and maximizing topic and style diversity for broad applicability.
Performance Metrics:
OpenAI o3-mini consistently delivers impressive performance as illustrated by the following benchmarks:
📚 Usage & Access
Code Samples:
The OpenAI o3-mini model is readily available on the AI/ML API platform. You can access it as "OpenAI o3 mini".
<snippet data-name="open-ai.chat-completion-o1" data-model="o3-mini"></snippet>API Documentation:
For comprehensive technical details and integration guides, refer to the detailed API Documentation here.
🔒 Ethical Guidelines & Licensing
OpenAI is committed to ethical AI development, emphasizing transparency regarding o3-mini's capabilities and limitations. Users are encouraged to adhere to responsible usage practices to prevent misuse or the generation of harmful content.
OpenAI o3-mini is distributed under an open-source MIT license. This license grants extensive rights for both research and commercial applications, provided ethical standards and creator rights are upheld.
Ready to integrate OpenAI o3-mini?
Get the OpenAI o3-mini API here
❓ Frequently Asked Questions (FAQ)
Q1: What is OpenAI o3-mini's primary focus?
A1: OpenAI o3-mini is primarily focused on enhancing reasoning capabilities for tasks requiring structured logic and precise prompt adherence, making it excellent for programming, mathematics, and logical challenges.
Q2: What is the context length capacity of o3-mini?
A2: The model can process an extended context length of up to 200,000 tokens, allowing for deep interactions and extensive information retention.
Q3: Is OpenAI o3-mini an open-source model?
A3: Yes, it is released under an MIT license, promoting broad usage, modification, and community engagement.
Q4: When was the knowledge cutoff for the o3-mini model?
A4: The model's knowledge is current as of January 2025.
Q5: Where can developers access the OpenAI o3-mini API?
A5: The API for OpenAI o3-mini is available on the AI/ML API platform.
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