O4 Mini Batch
Neural Network
O4 Mini Batch is a compact reasoning model from OpenAI with tool support and multimodal input in the o4 mini API.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 200 000 tokens
- Max answer length
- 100 000 tokens
- Reasoning effort
- low, medium, high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works O4 Mini Batch?
O4 Mini Batch is a compact o-series model from OpenAI, built around reasoning for fast and cost-effective operation. It supports tool calling, accepts multimodal input, and handles agentic scenarios where the response is built from multiple steps and function calls. Batch mode is suitable where requests can be sent in a stream and results retrieved later. Through BotHub, the model is accessible from Russia without a VPN or foreign card; payment is in rubles based on actual token usage, and unused tokens do not expire. In the same window, there are over 250 neural networks: answers are easy to compare, and a unified OpenAI-compatible API allows switching models in a multi-model system without rewriting the integration. Chats are encrypted via AES-GCM, and for companies, contracts, invoices, electronic document management (EDO), and an admin panel with limits are available. Developers will find this model useful for agents and function call chains, analysts for step-by-step data and hypothesis analysis, product teams for mass processing of similar requests in batches, and those working with mixed content for tasks where text and images arrive in a single request.Frequently asked questions about O4 Mini Batch
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
o4-mini:batch is a compact reasoning model from OpenAI. It is suitable where logic analysis is needed: math and STEM, code analysis and refactoring, debugging, and working with documents. It accepts text, images, and documents as input, and a 200,000-token context allows handling voluminous materials.
The output limit is 100,000 tokens per response. This is more than enough for a large code module, a detailed technical analysis, or a comprehensive report. Keep in mind that the model spends part of the budget on internal reasoning before formulating the final result.
Yes, this is a confirmed capability. The model first builds a chain of reasoning: it breaks the task into steps, checks intermediate conclusions, and only then answers. Therefore, it confidently handles math, logic problems, and code analysis where the sequence of steps is more important than speed.
Yes, function calling and structured JSON output are supported. The model can be embedded into agents, connected to external services, and provide predictable responses according to a specified schema. In BotHub, everything works via a unified OpenAI-compatible API, so you can switch models without rewriting the integration.
Images and documents — yes: send a screenshot, diagram, chart, or file and ask to analyze the content. There is no information about audio and video in our data, so count primarily on text, images, and documents as the main input formats.
We do not have specific data on languages, so it is most reliable to test the model with your own requests. You only pay for the tokens actually used, without a subscription, and they do not expire. Access is open without a VPN or foreign card, and payment is accepted via Russian cards.