Gemini 3.5 Flash
Neural Network
Google's Gemini 3.5 Flash: a multimodal model for code, reasoning, and agent tasks, available via a unified BotHub API.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 65 536 tokens
- Reasoning effort
- minimal, 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 3.5 Flash?
Gemini 3.5 Flash is Google's multimodal model that brings code and reasoning quality to the Pro level while remaining in the fast and cost-effective Flash class. It is optimized for programming and parallel agent task execution, with a batch variant designed for processing large volumes of requests. Through BotHub, the model is accessible without a VPN or foreign card: you pay with a local card based on actual token usage, with no subscription. Over 250 models are available in one window for easy switching and comparison, and a unified OpenAI-compatible API allows you to swap models in your service without rewriting integrations. Chats are encrypted via AES-GCM, and teams have access to contracts, invoices, electronic document management, and an admin panel with limits. Developers can use Gemini 3.5 Flash for writing and refactoring code, debugging, and reviewing changes; agent system engineers for parallel execution branches; analysts for batch document and log parsing; and product teams for rapid prototyping where response speed and cost are critical.Frequently asked questions about Gemini 3.5 Flash
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-3.5-flash:batch works with text, documents, and images: it parses files, answers questions about content, writes and edits code, and solves reasoning tasks. With a context window of up to 1,048,576 tokens, the model is suitable for long materials and batch processing of large request volumes.
The limit for a single response is 65,536 tokens. This is enough for voluminous text, a detailed document analysis, or an entire large code file. If you need more, break the task into parts: the 1,048,576-token context allows you to keep everything written previously in the dialogue.
Yes, the model's reasoning before answering is confirmed: it can break down a task into steps, verify intermediate conclusions, and only then provide a result. This is noticeable in math, logic, code analysis, and long documents where it is important not to miss a detail.
Yes, both. The model can call functions and return structured JSON, making it easy to integrate into agents and pipelines. In BotHub, it is a unified OpenAI-compatible API: you don't need to rewrite the integration scheme when switching to another model.
Images and documents — yes, this is confirmed: you can send a scan, PDF, table, or screenshot and request an analysis, summary, or structural formatting. There is no mention of audio or video in our data, so it is better to test this scenario with a small file.
The Gemini family is inherently multilingual, and Russian is among the languages these models work with. There are no separate measurements by language in our data, so evaluate it on your own task: in BotHub, you can immediately compare the response with another model in one window, without a VPN or foreign card.