Gemini 3.6 Flash
Neural Network
Gemini 3.6 Flash by Google is a model for coding, agentic workflows, and web development.
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, Batch API
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
Image prompt
(per 1K tokens)
How it works Gemini 3.6 Flash?
Gemini 3.6 Flash is a high-performance Google model for programming, agentic scenarios, and web application development. It is designed for precise results: it provides ready-to-use code immediately with fewer edits and revisions, making it suitable for autonomous agents and repetitive pipelines. In BotHub, the model is available without a VPN or foreign card: you pay with a Russian card in rubles only for tokens actually used; no subscription is needed, and tokens do not expire. Over 250 models are gathered in the same window, making it easy to switch between them and compare answers for a single task. A unified OpenAI-compatible API allows you to change models without rewriting the integration, chats are encrypted via AES-GCM, and for teams, there is a contract, invoice, electronic document management, and an admin panel with limits. It is useful for building an interface prototype and bringing it to a working state, analyzing an unfamiliar repository and performing refactoring, finding the cause of an error via stack trace, building an agent that follows a chain of steps, or automating routine support and internal analytics tasks.Frequently asked questions about Gemini 3.6 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.
This is a Google model for text processing: code analysis and writing, reasoning and STEM tasks, document and image parsing. The context is 1,048,576 tokens, so voluminous reports, logs, and entire repositories fit into a single request. It is suitable for both chat and API integrations.
In a single response, the model outputs up to 65,536 tokens — this is dozens of pages of text or a large code file in its entirety. If you need a larger volume, break the task into parts and continue the dialogue: the context of over a million tokens allows you to keep the entire conversation.
Yes, reasoning before answering is a stated capability of the model: it breaks down the task step-by-step before providing a result. This helps with math, logic, code analysis, and multi-step instructions where the correct solution path is more important than a quick answer.
Yes, the model supports function calling and structured JSON output. You describe the tools, it decides when to apply them, and returns the answer in the specified schema. In BotHub, this is available via a unified OpenAI-compatible API — you can switch models without rewriting the integration.
Images and documents — yes: according to our data, the model accepts them as input and responds with text, with documents processed natively. There are no notes in our data regarding audio and video, so we will not promise such input — check it with your task or choose a specialized model.
There are no specific notes about languages in our data, so we will not promise anything specific. The most honest way is to test it with your prompts: in BotHub, all models are gathered in one window, making it easy to compare answers and choose the right one. Access from Russia without VPN or foreign cards.