Gemini 3.7 Flash Batch
Neural Network
Gemini 3.7 Flash is Google's multimodal model for agentic tasks, coding, and multi-step reasoning via the BotHub API.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 65 536 tokens
- Reasoning effort
- low, medium, high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
Image prompt
(per 1K tokens)
How it works Gemini 3.7 Flash Batch?
Gemini 3.7 Flash is a multimodal model from Google designed for fast agentic scenarios, coding, and complex multi-step reasoning. The model is optimized for tasks requiring responsiveness and reliability in long chains of steps, accepting inputs beyond just text. The batch variant is designed for batch processing, allowing requests to be sent as an array rather than individually. Through BotHub, you can access Gemini 3.7 Flash from Russia without a VPN or foreign card, paying in rubles for tokens used, with no subscription. A unified OpenAI-compatible API allows switching between models without rewriting integrations. Data is encrypted via AES-GCM and not stored. Companies have access to contracts, invoices, electronic document management (EDO), and an admin panel with limits. For developers, this means code generation, refactoring, debugging, and review; for analysts, multi-step calculations and document processing; for product teams, building agentic scenarios with long sequences of steps; and for researchers, rapid hypothesis testing and comparing model responses in one window.Frequently asked questions about Gemini 3.7 Flash 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.
gemini-3.7-flash:batch is a version of the Google Gemini model with the 'batch' suffix, designed for batch processing large volumes of requests. It works with text, images, and documents, and supports reasoning, function calling, and structured JSON output. It is suitable for document analysis, data extraction, code parsing, and mass repetitive tasks via BotHub.
In a single response, gemini-3.7-flash:batch outputs up to 65,536 tokens—usually enough for a document chapter, a detailed report, or a large code fragment. The model's context reaches 1,048,576 tokens, so it retains long source materials without losing them during the dialogue.
Yes, reasoning before answering is marked in the model data as a confirmed capability. gemini-3.7-flash:batch breaks down tasks step-by-step, which significantly helps with multi-step calculations, logical reasoning, and code analysis. It makes sense to enable this mode where the solution process itself is important.
Yes, gemini-3.7-flash:batch supports function calling and structured JSON output. This means the model can be connected to external services and databases, and its responses can be received immediately as ready-made fields, without manual text parsing. This is convenient when building multimodal systems via the BotHub OpenAI-compatible API.
In addition to text, the model accepts images and documents: you can upload a scan, PDF, or spreadsheet and ask it to analyze the content. There is no indication of audio or video support in our data, so we will not promise it—it is worth checking with your own file directly in BotHub.
There is no separate evaluation of Russian language performance in the model data, so we will not make specific conclusions. The model is designed for text tasks and document analysis, so it is reasonable to test Russian-language scenarios—summarization, editing, data extraction—with your own examples in BotHub before implementation.