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
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