Explore OpenAI's o3 Deep Research Model

Tackle complex research tasks with advanced multimodal capabilities.

Input: image · text · file Output: text Context: 200,000 tokens Release: 2025-10-10

OpenAI's o3 Deep Research model, released in October 2025, is designed for tackling complex, multi-step research tasks. With a substantial context window of 200,000 tokens, it effectively handles both text and image inputs, producing detailed text outputs. This model is particularly suited for deep research applications, utilizing advanced tools like 'web_search' to enhance its capabilities. Users can fine-tune outputs using parameters like frequency_penalty, temperature, and more, making it a versatile choice for structured and unstructured data analysis.

Use Cases

Here are a few ways teams apply OpenAI: o3 Deep Research in practice—from fast drafting to multimodal understanding. Adapt these ideas to your workflow.

Conduct in-depth research analysis

Integrate structured data insights

Enhance multi-step research projects

Utilize web search for detailed findings

Key Features

A quick look at the capabilities that make this model useful in real projects.

Handles text and image inputs

200,000 token context window

Advanced research tool integration

Customizable output parameters

Text-focused output generation

Specs

Overview

Vendor
openai

Model ID
openai/o3-deep-research

Release
2025-10-10

Modalities & context

Input
image · text · file

Output
text

Context
200,000 tokens

Parameters & defaults

Supported parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, presence_penalty, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p

Defaults: temperature 0.2, top_p 0.95

Benchmark tests: OpenAI: o3 Deep Research

We ran this model against a few representative prompts to show its range. Review the outputs below and be the judge.

Text

Prompt:

Write 150 words on how AI might positively upend work, leisure and creativity

The OpenAI o3 Deep Research model is designed for advanced natural language processing tasks, offering capabilities in text generation, summarization, and contextual understanding. It is particularly suited for applications in academic research, content creation, and data analysis, allowing users to generate coherent and contextually relevant text based on provided prompts. The model can handle a variety of topics, making it versatile for different fields, including science, technology, and humanities. Notable constraints include potential limitations in understanding highly specialized jargon or niche subjects, which may affect the accuracy of generated content. Additionally, the model's performance can vary based on the complexity of the input and the specificity of the request. Users should be aware that while the model aims to produce high-quality outputs, it may occasionally generate incorrect or misleading information, necessitating human oversight for critical applications.

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Image

Prompt:

Generate an SVG of a pelican riding a bicycle.

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up.end

/ˌəpˈend/

verb

To “upend” means to completely disrupt, overturn, or drastically change the established order or structure of something. It implies a significant shift or alteration that can potentially have far-reaching consequences. When something is upended, it is turned upside down or transformed in a way that challenges conventional norms or expectations. The term often carries a sense of innovation, transformation, and sometimes even a hint of upheaval, indicating that the changes are not just minor adjustments but rather a fundamental reimagining of the status quo.

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