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OpenAI o1

OpenAI o1

o1 · Family: OpenAI o-series
OpenAI o-series reasoning model trained with reinforcement learning; builds an internal chain of thought before answering. Excels at math, science and coding.
✓ Active✓ Public accessReasoning modelMultimodalLLM📁 OpenAI o-series
Context window
200K
tokens
Max output
100,000
tokens
Release date
5 December 2024
Access:APIHostedDeployment:☁ Cloud

Overview

OpenAI o1 is a reasoning model from the “o” family, introduced on 12 September 2024 as o1-preview and released as the full o1 model on 5 December 2024. Unlike classic GPT models, o1 is trained with reinforcement learning to generate an extended internal chain of thought before producing an answer.

The model achieves strong results on tasks requiring complex reasoning — mathematics, hard sciences and programming. It scored 83% on AIME 2024 and ranks in the 89th percentile on Codeforces competitive-programming contests. On the GPQA Diamond benchmark it exceeds the accuracy of PhD-level human experts in physics, chemistry and biology.

o1 accepts text and images as input and produces text output. It supports a 200,000-token context window and can generate up to 100,000 tokens in a single response. Its knowledge cutoff is October 2023. It supports function calling, structured outputs and streaming; fine-tuning is not supported.

The model is available commercially through the OpenAI API and Microsoft Azure, as well as in ChatGPT products. API pricing is USD 15 per 1M input tokens, USD 7.50 per 1M cached input tokens and USD 60 per 1M output tokens.

Classification
Reasoning modelMultimodalLLM
Access & deployment
APIHosted
Cloud
Weights: Closed
Key parameters
📏 Context: 200K
Tools
📥 Input: text, image

Technical specification

Context window
200K
tokens
Max output tokens
100,000
tokens per response
Knowledge cutoff
1 Oct 2023
Knowledge boundary
License
Proprietary
Features:Tool use
Modalities
⬇ Input
textimage
⬆ Output
text

Capabilities and applications

Native model capabilities
Advanced reasoning
The ability to perform multi-step, structured reasoning: analysing problems, planning steps, and drawing conclusions from hypotheses. Reasoning-first models (e.g. GPT-5.1 Thinking) dedicate a portion of inference to chains of thought before responding.
Category: reasoning
Mathematical reasoning
The model's ability to solve mathematical tasks requiring multi-step reasoning — equations, proofs, combinatorics, geometry, calculus and competition-level problems.
Category: reasoning
Multi-step reasoning
Carrying out multi-step chains of reasoning across long, complex tasks.
Category: reasoning
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Image understanding
Analysing and interpreting the content of images.
Category: vision
Structured output
Producing data in structured formats such as JSON.
Category: structured_generation
Tool use
The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.
Category: planning
Long context
Support for large context windows — tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.
Category: language

Benchmark results

3 benchmarks
AIME 2024
accuracy · single sample
83%
📅 12 Sept 2024📄 OpenAI (Learning to Reason with LLMs)
Codeforces
percentile
89th percentile
📅 12 Sept 2024📄 OpenAI (Learning to Reason with LLMs)
GPQA
accuracy
📄 OpenAI
On GPQA Diamond, o1 exceeds the accuracy of PhD-level human experts in physics, chemistry and biology.

Pricing

Technical architecture

Core Architecture
Training Techniques

Deployment and security

🔒 Security / Enterprise
✓ Verified enterprise information
Updated: 23 Jul 2026↗ Security documentation