128K
0.00315
0.0126
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OpenAI o1-mini

OpenAI o1-min APIi: A cost-effective AI model optimized for STEM reasoning, offering advanced capabilities in mathematics and coding for developers.
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OpenAI o1-mini

Cost-efficient OpenAI reasoning model excelling in math and coding tasks.

Model Overview Card for OpenAI o1-mini

Basic Information

  • Model Name: OpenAI o1-mini
  • Developer/Creator: OpenAI
  • Release Date: September 12, 2024
  • Version: o1-mini-2024-09-12
  • Model Type: Large Language Model (LLM)

Description

Overview

OpenAI o1-mini is a cost-efficient reasoning model optimized for STEM tasks (science, technology, engineering, and math), particularly excelling in mathematics and coding. It offers advanced reasoning capabilities at a fraction of the cost of its larger counterpart, o1-preview.

Key Features
  • Enhanced performance in STEM reasoning tasks
  • Cost-effective alternative to o1-preview (80% cheaper)
  • Improved speed compared to o1-preview
  • Strong capabilities in coding and mathematical problem-solving
  • Incorporates chain-of-thought reasoning
Intended Use

o1-mini is designed for applications requiring focused reasoning without extensive world knowledge, particularly in:

  • Complex code generation and analysis
  • Advanced mathematical problem-solving
  • Scientific research and data analysis
  • Educational tools for STEM subjects
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

128,000 tokens

Max Output Tokens

65,536 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

o1-mini utilizes a transformer-based architecture optimized for STEM reasoning. It employs large-scale reinforcement learning to perform chain-of-thought reasoning, similar to the o1-preview model but with a smaller parameter count.

Training Data
  • Data Source and Size: Trained on a vast dataset up to October 2023
  • Knowledge Cutoff: October 2023
Performance Metrics
  • Scored 70.0% on the AIME (American Invitational Mathematics Examination)
  • Achieved a 1650 Elo rating on Codeforces (86th percentile)
  • Excels in HumanEval coding and high-school cybersecurity challenges
  • Surpasses GPT-4o on some reasoning benchmarks like GPQA and MATH-500

Comparison to Other Models

Accuracy
  • Closely matches o1-preview performance on AIME and Codeforces
  • Outperforms GPT-4o on reasoning-heavy tasks in STEM domains
  • Less effective than GPT-4o in language-focused tasks and broad world knowledge
Speed
  • 3-5 times faster than GPT-4o in reaching conclusions for complex reasoning tasks
  • Output speed: 73.9 tokens per second
  • Time to First Token (TTFT): 13.98 seconds
Robustness
  • Demonstrates 59% greater jailbreak resilience on the StrongREJECT dataset compared to GPT-4o
  • Handles diverse inputs well within its STEM specialization

Usage

Code Sample
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
  • Trained using the same alignment and safety techniques as o1-preview
  • Undergone comprehensive testing, red-teaming, and collaboration with U.S. and U.K. AI Safety Institutes
  • Designed to adhere to ethical guidelines and safety protocols embedded in its reasoning process

Price

OpenAI o1-mini is available through AI/ML API services. Pricing is set at $0.0031500 per 1K input tokens and $0.0126 per 1K output tokens, making it 80% cheaper than o1-preview.

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