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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: 'o1-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="o1-mini",
messages=[
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
-
AI Playground
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Product Detail
✨ Introducing OpenAI o1-mini: Your STEM-Focused Reasoning Powerhouse
OpenAI o1-mini is an innovative, highly efficient AI model specifically engineered for complex STEM (Science, Technology, Engineering, and Mathematics) reasoning. This model excels in critical areas such as advanced mathematics, sophisticated coding, and scientific problem-solving. Designed as a cost-effective alternative to larger models, o1-mini delivers robust chain-of-thought reasoning, ensuring faster response times and significantly reduced operational costs. It is an ideal solution for demanding research, education, and technical workflows requiring precision and efficiency.
⚙️ Technical Specifications
- Architecture: Transformer-based, specifically optimized for large-scale STEM reasoning tasks.
- Reasoning: Enhanced with reinforcement learning to support advanced chain-of-thought problem-solving capabilities.
- Knowledge Cutoff: October 2023, ensuring up-to-date knowledge based on diverse training datasets.
- Context Window: A generous 128K token context window.
- Max Output: Up to 65K tokens, accommodating extensive responses and complex code generations.
📈 Performance Benchmarks: Setting New Standards
- Achieved an impressive 70.0% on the American Invitational Mathematics Examination (AIME).
- Attained a 1650 Elo rating on Codeforces, placing it in the 86th percentile among competitive programmers.
- Demonstrates superior performance over GPT-4o on critical STEM reasoning benchmarks, including GPQA and MATH-500.
- Exhibits strong capabilities in HumanEval coding and cybersecurity challenges, proving its robustness in practical applications.
Visual representation of OpenAI o1-mini's benchmark achievements.
💰 API Pricing: Cost-Effective Excellence
- Input Tokens: $0.00315 per 1,000 tokens
- Output Tokens: $0.0126 per 1,000 tokens
- o1-mini is approximately 80% cheaper than the larger o1-preview model, offering unparalleled value for its capabilities.
💡 Key Capabilities of OpenAI o1-mini
- 🔬 STEM Reasoning: Delivers high precision in complex mathematics, advanced coding, and detailed scientific analysis through sophisticated chain-of-thought methods.
- 💲 Cost Efficiency: Provides advanced reasoning power with significantly reduced computational expenses, making high-performance AI accessible.
- ⚡ Speed: Achieves 3 to 5 times faster performance in complex reasoning tasks compared to GPT-4o, with an impressive 73.9 tokens/second output speed.
- 💻 Coding Expertise: Capable of generating, analyzing, and debugging advanced code reliably across multiple programming languages.
- 🛡️ Robustness: Demonstrates improved resistance against jailbreak attempts on strong adversarial datasets, ensuring safer deployments.
🚀 Optimal Use Cases
- Software Development: Ideal for complex software development and technical code debugging.
- Education & Learning: Powers advanced STEM education tools and innovative learning platforms.
- Scientific Research: Facilitates scientific data analysis and mathematical research with high accuracy.
- Cost-Sensitive Deployments: Perfect for applications requiring focused reasoning accuracy without the need for broad world knowledge, prioritizing cost-efficiency.
🧑💻 Code Sample
Below is an example of how you might interact with the OpenAI o1-mini model via its API:
Note: The `
🆚 Comparison with Other Models
- vs OpenAI o1-preview: o1-mini matches the core STEM reasoning accuracy of o1-preview while being 80% more cost-effective and significantly faster.
- vs GPT-4o: o1-mini outperforms GPT-4o in specialized STEM reasoning and speed, though GPT-4o remains more effective in broad language tasks.
📚 API Documentation & Resources
For comprehensive details on integrating and utilizing OpenAI o1-mini, refer to the official documentation:
✅ Ethical Guidelines & Safety Commitments
- Trained using the same rigorous alignment and safety techniques as the o1-preview model.
- Undergone comprehensive testing, including extensive red-teaming, and collaborative efforts with leading U.S. and U.K. AI Safety Institutes.
- Designed to strictly adhere to ethical guidelines and robust safety protocols embedded within its core reasoning processes.
❓ Frequently Asked Questions (FAQ)
Q1: What is OpenAI o1-mini primarily designed for?
A1: OpenAI o1-mini is primarily designed as a highly efficient, cost-effective reasoning model focused on STEM tasks, including mathematics, coding, and scientific problem-solving.
Q2: How does o1-mini compare in cost to other OpenAI models?
A2: o1-mini is significantly more cost-effective, being approximately 80% cheaper than the larger o1-preview model for similar STEM reasoning accuracy.
Q3: Can o1-mini handle complex coding tasks?
A3: Yes, o1-mini excels in coding expertise, capable of generating, analyzing, and debugging advanced code reliably across multiple languages.
Q4: What are the main advantages of using o1-mini over GPT-4o for STEM tasks?
A4: o1-mini outperforms GPT-4o in specialized STEM reasoning and speed, offering faster task completion and higher accuracy in these specific domains.
Q5: What is the knowledge cutoff for OpenAI o1-mini?
A5: The knowledge cutoff for OpenAI o1-mini is October 2023, based on the diverse datasets it was trained on up to that period.
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