OpenAI o1-preview API

OpenAI o1-preview Description

Basic Information

• 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 an advanced language model designed for complex reasoning and problem-solving tasks, particularly excelling in science, coding, and mathematics.

Key Features

• Chain-of-Thought (CoT) reasoning capabilities
• Enhanced performance in coding and mathematical tasks
• Self-fact-checking abilities
• Improved safety measures and alignment

Intended Use

The o1-preview model is intended for applications requiring deep reasoning and can accommodate longer response times. It's particularly useful for:

• Complex code generation and analysis
• Advanced mathematical problem-solving
• Comprehensive brainstorming sessions
• Multifaceted document comparison

OpenAI o1-preview can be used for symptoms analysis and can assist in narrowing and identifying overlooked differential diagnoses and help interpret complex test results. Learn more about this and other models and their applications in Healthcare here.

Language Support

While specific language support details are not explicitly mentioned, the model demonstrates strong performance across various languages, including low-resource languages.

Context Window

The context window size is 128000 tokens.

Max Output Tokens

The maximum output token limit for o1-preview is 32,768 tokens.

Beta Limitations

During the beta phase, many chat completion API parameters are not yet available. Most notably:

• Modalities: text only, images are not supported.
• Message types: user and assistant messages only, system messages are not supported.
• Streaming: not supported.
• Tools: tools, function calling, and response format parameters are not supported.
• Logprobs: not supported.
• Other: temperature, top_p and n are fixed at 1, while presence_penalty and frequency_penalty are fixed at 0.
• Assistants and Batch: these models are not supported in the Assistants API or Batch API.

Technical Details

Architecture

The o1-preview model utilizes a transformer-based architecture with significant enhancements in reasoning capabilities. It employs large-scale reinforcement learning to perform chain-of-thought reasoning.

Training Data

• Data Source and Size: Trained on a vast dataset up to October 2023
• Knowledge Cutoff: October 2023

Performance Metrics

• Achieved 83% accuracy on a qualifying exam for the International Mathematics Olympiad
• Scored in the 89th percentile on Codeforces (Competitive Programming)
• Surpassed human PhD-level performance on the GPQA diamond benchmark in physics, chemistry, and biology

Comparison to Other Models

• Accuracy: Outperforms GPT-4o on most reasoning-heavy tasks
• Speed: Slower than previous models like GPT-4o, as it "thinks before it answers"
• Robustness: Demonstrates improved performance with strategic test-time submissions

Usage

Code Samples

API Documentation

For detailed API documentation, visit: https://docs.aimlapi.com/quickstart/setting-up

Reasoning models https://platform.openai.com/docs/guides/reasoning

Ethical Guidelines

OpenAI has implemented enhanced safety measures for the o1-preview model:

• Improved performance on internal safety tests compared to GPT-4o
• New safety training approach harnessing reasoning capabilities
• Collaboration with U.S. and U.K. AI Safety Institutes for evaluation and testing

Price

The o1-preview model is available through AI/ML API services. Pricing is set at $0.01575 per 1K input tokens and $0.063 per 1 K output tokens.

• Advanced scientific/math solvers: Ideal for complex problem domains similar to PhD-level reasoning.
• Expert coding analysis: Great for nuanced programming challenges and debugging.
• Critical domain reasoning: Use in fields like biomedicine, law, or finance that entail deep logical chains.
• Transparent chain-of-thought systems: For applications needing explainable reasoning tokens.
• High-stakes decision tools: Where logical consistency and safety alignment are crucial.

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