ReasoningOpenAI

o1

o1 is OpenAI's reasoning-focused model that uses deliberative problem-solving for complex tasks in math, science, and coding with a 200K token context window.

Context 200K
Tier Reasoning
Knowledge Jun 2024
Tools Supported
Input from
$15.00 / 1M tokens
across 2 providers

API Pricing

ProviderInput / 1MOutput / 1MUpdated
$15.00$60.004/14/2026
$15.00$60.003/31/2026

Prices updated daily. Last check: 4/14/2026

Model Details

General

Creator
OpenAI
Family
o-series
Tier
Reasoning
Context Window
200K
Knowledge Cutoff
Jun 2024
Modalities
Text

Capabilities

Tool Calling
Yes
Open Source
No
Subtypes
Chat Completion

Strengths & Limitations

  • Multi-step reasoning process for complex problem-solving
  • 200,000 token context window for extensive document analysis
  • Tool calling support for integration with external systems
  • Specialized architecture optimized for mathematical and scientific reasoning
  • June 2024 knowledge cutoff provides recent information access
  • Deliberative approach reduces reasoning errors in complex tasks
  • Strong performance on coding challenges requiring systematic analysis
  • Text-only modality - no image or audio input support
  • Proprietary model with weights not publicly available
  • Reasoning process introduces latency compared to standard completion models
  • No vision capabilities unlike multimodal alternatives
  • Limited to chat completion format rather than general text completion

Key Features

200K token context window
Multi-step reasoning architecture
Tool calling with function execution
Chat completion interface
Text-based problem solving
Deliberative response generation
Mathematical reasoning capabilities
Scientific analysis processing

About o1

o1 is OpenAI's reasoning-specialized model from the o-series family, designed to approach complex problems through deliberative thinking rather than immediate response generation. Unlike traditional language models that generate tokens sequentially, o1 employs a multi-step reasoning process to work through challenging tasks before producing its final output. The model supports text-only interactions with a 200,000 token context window and includes tool calling capabilities. o1 demonstrates particular strength in mathematical reasoning, scientific problem-solving, and coding challenges where step-by-step analysis is beneficial. The model's knowledge cutoff is June 2024, providing access to relatively recent information for reasoning tasks. o1 is positioned for applications requiring careful analysis rather than rapid response generation, making it suitable for research assistance, complex problem decomposition, and technical workflows where accuracy is prioritized over speed. The model represents OpenAI's approach to enhancing reasoning capabilities through deliberative processing architecture.

Common Use Cases

o1 is designed for applications requiring careful analysis and multi-step reasoning rather than rapid content generation. It excels in mathematical problem-solving, scientific research assistance, complex coding challenges, and analytical tasks where accuracy is more important than response speed. The model is well-suited for educational applications involving step-by-step problem breakdowns, research workflows requiring systematic analysis, and technical documentation where thorough reasoning is essential. Its deliberative approach makes it particularly valuable for tasks like theorem proving, algorithm design, scientific hypothesis evaluation, and complex data analysis where traditional completion models might rush to conclusions.

Frequently Asked Questions

How much does o1 cost per million tokens?

o1 pricing varies by provider and may include different rates for reasoning computation versus standard tokens. Check the pricing table above for current rates across all providers.

What is o1 best used for?

o1 is optimized for complex reasoning tasks including mathematical problem-solving, scientific analysis, coding challenges, and research assistance where step-by-step thinking is more valuable than rapid response generation.

How does o1's reasoning process differ from standard language models?

o1 uses a deliberative multi-step reasoning architecture that works through problems systematically before generating responses, rather than producing tokens sequentially. This approach introduces latency but can improve accuracy on complex analytical tasks.