OpenAI: o3-deep-research
o3-deep-research is a reasoning model from OpenAI released on Jun 27, 2025. It reads text and writes text, with a 200K token context window and up to 100K tokens per reply.
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It costs $9 per million input tokens and $36 per million output tokens. Cached input bills at $2.20, 76% less than fresh input.
It supports tool calling and prompt caching.
- Modalities
- In / out price
- $9 / $36 per 1M
- Context
- 200K
- Max output
- 100K
- Released
- Jun 27, 2025
- Knowledge cutoff
- n/a
- Tool calling
- Structured output
- Reasoning
- Prompt caching
- Open weights
- Free to use
o3-deep-research pricing
USD, as listed on Oct 15, 2026. Most bills are input heavy, so the cached input rate matters as much as the headline price.
| Input | $9 / 1M tokens | |
|---|---|---|
| Output | $36 / 1M tokens | |
| Cached input (read) | $2.20 / 1M tokens | 76% off input |
What o3-deep-research costs per month
Pick a workload close to yours or type your own volumes.
Supported request parameters
What you can set when you call o3-deep-research through an OpenAI compatible API.
- Tools
tools - Reasoning
reasoning
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o3-deep-research questions
How much does the o3-deep-research API cost?
$9 per million input tokens and $36 per million output tokens, with cached input at $2.20. The estimator on this page turns that into a monthly bill for your own workload.
What is the context window of o3-deep-research?
200,000 tokens of input, and it can write up to 100,000 tokens in one reply. As a rough guide, a million tokens is around 750,000 English words.
Does o3-deep-research support tool calling?
Yes. It accepts tool definitions, which is what an agent needs to call your APIs.
Is o3-deep-research open source?
No. OpenAI serves it through an API only, so you cannot download the weights or host it yourself.
When was o3-deep-research released?
Jun 27, 2025.
Catalog data from models.dev (MIT licensed), refreshed daily, last on Oct 15, 2026. The summary is written from these facts.
Building on o3-deep-research?
I test your real tasks on it and the two closest alternatives, then ship the agent on whichever wins on cost and accuracy.
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