OpenAI o1-preview API: A powerful language model with enhanced reasoning capabilities for tackling complex problems in science, coding, and mathematics.
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