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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-preview',
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-preview",
messages=[
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
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Product Detail
Basic Information: OpenAI o1-preview Model
- ✨ Model Name: OpenAI o1-preview
- 🛠️ Developer/Creator: OpenAI
- 📅 Release Date: September 12, 2024
- ➡️ Version: o1-preview-2024-09-12
- 🧠 Model Type: Large Language Model (LLM)
Overview
OpenAI o1-preview is a cutting-edge language model specifically engineered for sophisticated reasoning and intricate problem-solving across various domains. It particularly excels in scientific inquiry, robust coding, and advanced mathematical computations.
Key Features
- 💡 Chain-of-Thought (CoT) Reasoning: Advanced capabilities for step-by-step logical deduction.
- 💻 Enhanced Performance: Superior results in complex coding and mathematical tasks.
- ✅ Self-Fact-Checking: Built-in mechanisms to verify information accuracy.
- 🔒 Improved Safety & Alignment: Designed with stronger safety measures and better alignment with human values.
Intended Use Cases
The o1-preview model is ideally suited for applications that demand deep analytical reasoning and can accommodate longer processing times. Its core utility lies in:
- Complex code generation and thorough analysis.
- Advanced mathematical problem-solving.
- Comprehensive brainstorming sessions.
- Multifaceted document comparison and analysis.
Furthermore, OpenAI o1-preview is adept at symptoms analysis, assisting in narrowing down and identifying overlooked differential diagnoses, and interpreting complex test results within the healthcare sector. For further insights into its applications in healthcare, including identifying differential diagnoses and interpreting complex test results, explore: AI in Healthcare: Generative AI Uses & Examples.
Language Support
While explicit details regarding specific language support are not fully disclosed, the model consistently demonstrates strong performance across a diverse range of languages, including those with limited resources.
Context Window & Output Limits
- 📏 Context Window Size: 128,000 tokens
- ✍️ Maximum Output Tokens: 32,768 tokens
Beta Limitations
During its beta phase, several chat completion API parameters are currently unavailable for the o1-preview model. Key limitations include:
- Modalities: Supports text only; image inputs are not yet supported.
- Message Types: Limited to user and assistant messages; system messages are not supported.
- Streaming: Not supported.
- Tools & Functions: Tool utilization, function calling, and response format parameters are not supported.
- Logprobs: Not supported.
- Fixed Parameters: `temperature`, `top_p`, and `n` are fixed at `1`; `presence_penalty` and `frequency_penalty` are fixed at `0`.
- API Compatibility: These models are not supported in the Assistants API or Batch API.
Technical Details
Architecture
The o1-preview model is built upon a transformer-based architecture, incorporating significant enhancements to its reasoning capabilities. It leverages large-scale reinforcement learning to perform advanced chain-of-thought reasoning processes.
Training Data
- 📚 Data Source & Size: Trained on a vast dataset, current up to October 2023.
- ✂️ Knowledge Cutoff: October 2023.
Performance Metrics
- International Mathematics Olympiad: Achieved an impressive 83% accuracy on a qualifying exam.
- Codeforces (Competitive Programming): Scored in the 89th percentile.
- GPQA Diamond Benchmark: Surpassed human PhD-level performance in physics, chemistry, and biology.
Comparison to Other Models
- 🎯 Accuracy: Outperforms GPT-4o on the majority of reasoning-intensive tasks.
- ⏱️ Speed: Slower than previous models such as GPT-4o, primarily because it employs a deliberate "thinks before it answers" approach.
- 💪 Robustness: Exhibits enhanced performance and stability with strategic test-time submissions.
Usage & Integration
Code Samples
Integrating OpenAI o1-preview into your applications can be done via its API. Below is an illustrative code sample demonstrating basic interaction.
# Example Python Code for OpenAI o1-preview Chat Completion
import openai
client = openai.OpenAI()
response = client.chat.completions.create(
model="o1-preview",
messages=[
{"role": "user", "content": "Explain quantum entanglement in simple terms."}
],
# Note: During beta, parameters like temperature, top_p, tools, streaming are fixed or not supported.
# For detailed usage, refer to the API documentation.
)
print(response.choices[0].message.content)
API Documentation
For comprehensive API documentation and setup guides, refer to: Quickstart: Setting Up OpenAI Models.
Delve deeper into the principles of reasoning models here: OpenAI Reasoning Models Guide.
Ethical Guidelines & Safety
OpenAI has implemented enhanced safety measures for the o1-preview model, reflecting a strong commitment to responsible AI development:
- Improved Safety Performance: Demonstrated better results on internal safety tests compared to GPT-4o.
- Innovative Safety Training: Utilizes a new approach that harnesses the model's reasoning capabilities for enhanced safety.
- Collaborative Evaluation: Engages with U.S. and U.K. AI Safety Institutes for thorough evaluation and testing.
Pricing
The o1-preview model is accessible through AI/ML API services. The current pricing structure is set at:
- 📥 Input Tokens: $0.01575 per 1,000 input tokens.
- 📤 Output Tokens: $0.063 per 1,000 output tokens.
Frequently Asked Questions (FAQs)
Q1: What are the primary strengths of OpenAI o1-preview?
A1: The o1-preview model excels in complex reasoning and problem-solving, with particular strengths in science, coding, and mathematics, driven by its advanced Chain-of-Thought (CoT) reasoning capabilities.
Q2: How does o1-preview compare in performance to GPT-4o?
A2: While o1-preview is generally slower than GPT-4o due to its "thinks before it answers" approach, it outperforms GPT-4o on most reasoning-heavy tasks, demonstrating higher accuracy.
Q3: What are the main limitations during the o1-preview beta phase?
A3: Key beta limitations include text-only modality (no images), lack of support for streaming, tools/function calling, system messages, and fixed parameters for `temperature`, `top_p`, and `n`. It also isn't supported in the Assistants or Batch APIs.
Q4: What is the context window size and maximum output for o1-preview?
A4: The o1-preview model features a substantial context window of 128,000 tokens and can generate a maximum output of 32,768 tokens.
Q5: How is the o1-preview model priced?
A5: Pricing is set at $0.01575 per 1,000 input tokens and $0.063 per 1,000 output tokens, available via OpenAI's AI/ML API services.
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