GPT 5.5 Batch
Neural Network
GPT 5.5 Batch is an OpenAI model with over 1M token context for complex professional tasks and coding.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 050 000 tokens
- Max answer length
- 128 000 tokens
- Reasoning effort
- none, low, medium, high, extra high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works GPT 5.5 Batch?
GPT 5.5 is an OpenAI frontier model designed for complex professional workloads: compared to GPT 5.4, it reasons more accurately, works more stably, and consumes tokens more efficiently on difficult tasks. The context window exceeds one million tokens, allowing large repositories and long document sets to fit into a single request, while the batch version is designed for bulk processing. In BotHub, the model is accessible without a VPN or foreign card; payment is in rubles based on actual token usage, not a subscription. Over 250 other neural networks are available in the same window, allowing you to switch between them and compare answers. A unified OpenAI-compatible API allows you to connect the GPT 5.5 API and switch models later without rewriting the integration, while AES-GCM encryption protects your correspondence. Companies have access to contracts, invoices, electronic document management, and an admin panel with limits. Developers use the model for refactoring and debugging large codebases, analysts for calculations and STEM tasks, product teams for building agents with function calling, and editorial teams for processing document archives in batches.Frequently asked questions about GPT 5.5 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.
gpt-5.5:batch works with text, images, and documents, can reason before answering, and call functions. The context window is 1,050,000 tokens, making the model suitable for analyzing large codebases, long documents, analytics, refactoring, and debugging, as well as agent scenarios with tools.
The output limit is 128,000 tokens per response. This is enough for voluminous documentation, an entire code module, or a detailed technical analysis. If the response hits the limit, just ask it to continue, and the model will write the next part.
Yes, the reasoning mode is confirmed by our data: the model first builds a chain of steps and then provides the result. This significantly helps in mathematics and STEM tasks, code debugging, and situations where it is important to follow conditions sequentially rather than guessing the answer.
Yes, function calling and structured JSON output are supported. The model is available via the unified OpenAI-compatible BotHub API, making it easy to integrate into agents and multi-model systems, and to switch to another model if necessary without rewriting the integration.
According to our data, images and documents are accepted as input, including natively: you can send an interface screenshot, diagram, table, or PDF and ask to analyze the content. Audio and video input is not noted in our data, so count on text, images, and files.
Languages are not specified separately in our data, so it is more reliable to test the model on your own tasks — send a typical request and evaluate the result. The BotHub interface is in Russian, access is available from Russia without a VPN or foreign card, and payment is made with Russian cards in rubles.