GPT 5.1 Codex Max
Neural Network
GPT 5.1 Codex Max is an OpenAI agentic model for code: long-term development tasks and large context.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 400 000 tokens
- Max answer length
- 128 000 tokens
- Reasoning effort
- low, medium, high, extra high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works GPT 5.1 Codex Max?
GPT 5.1 Codex Max is an OpenAI agentic model designed for long-term coding work: it is built for tasks that don't fit into a single short prompt and maintains a large volume of context. It is based on the updated 5.1 reasoning stack and trained on agentic development scenarios, so the model handles tasks step-by-step rather than answering with a single reply. In BotHub, it is accessible from Russia without a VPN or foreign card; payment is made with Russian cards in rubles based on actual usage, and unused tokens remain on the balance. Over 250 models are available in one window, allowing you to compare answers and switch models via a unified OpenAI-compatible API without rewriting integrations. For teams, we offer contracts, invoices, and an admin panel with limits. It is suitable for refactoring a large module, analyzing an unfamiliar codebase, finding the cause of a floating bug, building a service prototype from scratch, or assigning the model a long chain of edits within a single task.Frequently asked questions about GPT 5.1 Codex Max
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.
This is an OpenAI Codex series model focused on coding: writing, refactoring, debugging, and analyzing third-party repositories. A 400,000-token context allows you to keep a large project along with documentation in front of the model, while effort and detail settings help choose the depth and volume of the response.
Up to 128,000 tokens per response — enough for a large module, extensive refactoring, or a detailed technical analysis with comments. In practice, the length depends on the task: a short question gets a short answer, while the model will complete the generation of a large file.
Yes, the reasoning mode is confirmed: the model first outlines the solution path and only then provides the result. This is noticeable in debugging, architectural questions, and STEM tasks. The effort parameter allows you to choose whether to answer faster or think longer — for simple edits and complex analyses, respectively.
Yes, the model supports function calling and structured JSON output, making it easy to integrate into agents, pipelines, and services where the response needs to be parsed programmatically. In BotHub, this works via a unified OpenAI-compatible API, so the model can be swapped without rewriting the integration.
According to our data, images and documents are accepted as input: you can send a screenshot of an error, an architecture diagram, an interface layout, or a specification file and receive a text analysis. There is no information about audio or video in our data, so expect to work with text, images, and documents.
Our data does not describe the model's performance with specific languages, so we won't make promises in advance — it's easier to test it on your task and compare the results with another model in the same window. The BotHub interface is in Russian, payment is via Russian cards, and no VPN or foreign card is required.