o4-mini-deep-research via OpenAI

Specifications

Context Window

200,000 tokens

Release Date

2024-06-26

Capabilities

AttachmentsReasoningTool callingImage input

Availability

Proprietary API

Model Overview

OpenAI is the creator of the GPT series and o-series reasoning models. Known for ChatGPT and the GPT-4 family, OpenAI offers some of the most widely used language models for general-purpose AI tasks, code generation, and multimodal understanding.

o4-mini-deep-research is a o-mini-family model by OpenAI with a 200k token context window and up to 100k output tokens. It is priced at $2.00/1M input tokens and $8.00/1M output tokens.

Key capabilities include: attachments, reasoning, tool calling, image input. It supports advanced reasoning for complex multi-step tasks. It can call external tools and functions for agentic workflows.

Details

ProviderOpenAI

Model IDo4-mini-deep-research

Familyo-mini

Release Date2024-06-26

Last Updated2024-06-26

Knowledge Cutoff2024-05

Context Window200,000 tokens

Max Output100,000 tokens

Input Cost / 1M$2.00

Output Cost / 1M$8.00

Cache Read / 1M$0.5000

Developer Resources

API Documentation OpenAI Pricing Page

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Frequently Asked Questions

How much does o4-mini-deep-research cost to use?

o4-mini-deep-research is priced at $2.00/1M input tokens and $8.00/1M output tokens. Use the cost estimator on this page to calculate your expected spend based on your usage pattern.

What is a token and how does it relate to pricing?

A token is a chunk of text — roughly ¾ of a word in English. For example, "chatbot" is two tokens. LLM API pricing is based on the number of tokens you send (input) and receive (output). Input tokens include your prompts, uploaded documents, and images, while output tokens are the model's generated responses.

Why are input and output tokens priced differently?

LLM providers charge separately for input and output tokens. Output tokens are typically more expensive because generating each token requires more compute — the model must run a full forward pass for every token it produces, while input tokens are processed in parallel.

What is the context window of o4-mini-deep-research?

o4-mini-deep-research supports a context window of 200,000 tokens. This is the maximum number of tokens (input + output combined) the model can process in a single request. Larger context windows let you send longer documents or maintain longer conversation histories.

How accurate is this cost estimation?

This tool provides a ballpark estimate based on per-token pricing. Actual costs may differ due to prompt caching, batched API calls, volume discounts, reasoning token overhead, and provider-specific billing rules. Use it for budgeting and comparison, not as an invoice prediction.

How does o4-mini-deep-research pricing compare to other models?

You can compare o4-mini-deep-research with other models on our LLM API pricing calculator. Use the cost estimator to see side-by-side cost breakdowns across different providers and models to find the best fit for your budget and requirements.

What factors affect my total API cost?

Your total cost depends on several factors: the number of API calls you make, the length of your prompts (input tokens), the length of generated responses (output tokens), whether you use features like image or document uploads (which add input tokens), and any provider-specific charges for caching or batch processing.

How can my team use o4-mini-deep-research via API?

You can connect your own OpenAI API key and give your entire team access to o4-mini-deep-research through TypingMind Teams. It lets you build a unified AI workspace where team members can use o4-mini-deep-research and other models — without needing their own API keys. You stay in control of usage limits, costs, and permissions, all from a single dashboard.