Gemini 3.8 Flash Batch
Neural Network
Gemini 3.8 Flash is Google's model for coding, agent scenarios, and multi-step reasoning, available via API in BotHub.
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 3.8 Flash Batch?
Gemini 3.8 Flash is a model from Google's Flash line, which has improved in programming, agent scenarios, and multi-step reasoning compared to Gemini 3.7 Flash. The "batch" suffix denotes batch request processing: this format is suitable for high-volume tasks where stable results on a large stream are more important than instant reactions. Connection via BotHub does not require a VPN or foreign card: payment is made with Russian cards in rubles, based on actual token usage, without a subscription. Over 250 models are gathered in one window on the website and in the Telegram bot, and a unified OpenAI-compatible API allows you to switch models without rewriting the integration. Data is encrypted, and companies have access to contracts, invoices, electronic document management (EDO), and an admin panel with limits. Use cases are clear: batch processing of thousands of requests, reviews, and documents, code review and refactoring, agents performing long chains of steps, preparing analytical summaries from an array of sources, and rough labeling of datasets for training.Frequently asked questions about Gemini 3.8 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.
This is a Google text model with a 1,048,576 token context: it analyzes long documents and images, reasons before answering, writes and fixes code, and calls functions. Suitable for large file analytics, customer support, data extraction, and agent scenarios.
The limit for a single response is 65,536 tokens, which is a lot: it can fit a detailed analysis, a long article, or a large code file. If the text still doesn't fit, continue generation in the next message; the million-token context can handle it.
Yes, reasoning before answering is a confirmed capability. The model first builds a chain of steps, then provides the result, so it makes fewer mistakes in math, logic, code analysis, and tasks requiring the synthesis of multiple conditions from a long document.
Yes, function calling and structured JSON output are supported: the model can choose the necessary tool itself and return the answer strictly according to your schema. In BotHub, this works via a unified OpenAI-compatible API, so you can switch models in your integration without rewriting code.
Images and documents — yes. You can attach them directly to the request: the model analyzes screenshots, diagrams, photos, and files with text and tables, and answers with text. Audio and video input is not noted in our data, so such tasks should be tested with your own example.
Our data does not specifically record the model's working languages, so we won't promise accuracy in Russian — it's better to run your typical request and compare. It's easy to do: BotHub offers access without VPN or foreign cards, with payment via Russian cards in rubles.