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o3-deep-research

o3-deep-research is a specialized variant of o3 designed for deep research tasks, capable of extended multi-step investigation, source synthesis, and comprehensive analysis on complex questions that require thorough exploration.

File InputImplicit CachingReasoningTool UseVision (Image)Web Search
index.ts
import { streamText } from 'ai'
const result = streamText({
model: 'openai/o3-deep-research',
prompt: 'Why is the sky blue?'
})

Playground

Try out o3-deep-research by OpenAI. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.

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Ask o3-deep-research anything to try it out.

Providers

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.

Provider
Context
Latency
Throughput
Input
Output
Cache
Web Search
Per Query
Capabilities
ZDR
No Training
Release Date
OpenAI
200K
$10.00/M$40.00/M
Read:$2.5/M
Write:—
$10.00/K
+ input costs
—
+4
06/26/2025
Throughput

P50 throughput on live AI Gateway traffic, in tokens per second (TPS). Visit the docs for more info.

Latency

P50 time to first token (TTFT) on live AI Gateway traffic, in milliseconds. View the docs for more info.

Uptime

Direct request success rate on AI Gateway and per-provider. Visit the docs for more info.

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About o3-deep-research

o3-deep-research was released on June 26, 2025 as a specialized variant of the o3 reasoning model, designed specifically for deep research tasks. While standard o3 applies chain-of-thought reasoning to produce answers, o3-deep-research extends this approach to conduct multi-step investigation, exploring questions from multiple angles, synthesizing information from diverse sources, and building comprehensive analyses.

The model spends significantly more time per request than standard models, reflecting the depth of investigation it performs. Each response represents the result of an extended research process rather than a single-pass generation. This makes it unsuitable for real-time applications but valuable for tasks where thoroughness and comprehensiveness matter.

If you need comprehensive answers to complex questions, o3-deep-research automates the kind of deep investigation that previously required hours of manual research and synthesis.

What To Consider When Choosing a Provider

  • Configuration: o3-deep-research is designed for tasks that require thorough research rather than quick answers. It spends significantly more time per request, conducting multi-step investigation before producing results.
  • Configuration: This is not a general chat model. It's built for deep analytical questions that benefit from extended exploration and synthesis.
  • Zero Data Retention: AI Gateway does not currently support Zero Data Retention for this model. See the documentation for models that support ZDR.
  • Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

When to Use o3-deep-research

Best For

  • Literature review: Comprehensive analysis of research topics with source synthesis
  • Due diligence research: Thorough investigation of companies, technologies, or markets
  • Complex question answering: Questions that require exploring multiple angles and synthesizing diverse information
  • Investigative analysis: Multi-step reasoning that builds on intermediate findings
  • Report generation: Producing comprehensive reports that require deep exploration of a topic

Consider Alternatives When

  • Quick answers: Standard o3 for reasoning tasks that don't require extended investigation
  • General chat: GPT-5 or GPT-5.2 for conversational interactions
  • Real-time responses: Any standard model when response time is constrained
  • Simple lookups: Search-augmented models for straightforward information retrieval

Conclusion

o3-deep-research brings automated deep research capability to AI Gateway, conducting the kind of thorough, multi-step investigation that complex questions demand. For analytical workloads where comprehensiveness matters more than speed, it provides a level of depth that standard models cannot match.