How it works Gemma 4 31B Instruct?
Gemma 4 31B Instruct is a dense Google DeepMind multimodal model with 30.7B parameters: it accepts text and images as input and outputs text. The 256K token context window allows you to work with large documents and entire codebases, while the switchable reasoning mode helps choose between a quick answer and a detailed task breakdown. Native function calling makes the model a convenient foundation for agents and integrations with external services. In BotHub, Gemma 4 is available from Russia without a VPN or foreign card; payment is made with Russian cards based on actual usage, and unused tokens do not expire. With over 250 models in one window, you can compare Gemma 4's answers with another model in a few clicks, and the unified OpenAI-compatible API allows you to switch models without rewriting integrations. For companies, we offer contracts, invoices, and electronic document management, and correspondence is protected by AES-GCM encryption. It is useful for developers for code review and refactoring, for analysts for parsing long reports, for product teams for reading screenshots and diagrams, for engineers for building agents with functions, and for researchers for STEM tasks with step-by-step reasoning.Frequently asked questions about Gemma 4 31B Instruct
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 262,144 token context window: it parses long documents and images, reasons before answering, and calls external functions. Suitable for analyzing materials, working with code, STEM tasks, and integration into services via the unified BotHub API.
The single response limit is 32,768 tokens. This is enough for a voluminous article, a detailed document analysis, or a large file with code in its entirety. If the task is even larger, break it into parts: the 262,144 token context allows you to keep the entire conversation in one dialogue.
Yes, the model has a reasoning mode: before the final answer, it breaks down the condition step-by-step. This significantly helps in mathematics, logic, and code debugging, where the sequence of conclusions is important. For simple questions, this approach may slightly increase waiting time.
Yes, the model supports function calling and structured JSON output. You can describe tools, receive a correct object from the model, and pass it on to your code. Via the BotHub OpenAI-compatible API, this connects just like other models — you won't have to rewrite the integration.
You can upload images and documents: send a screenshot, diagram, table, or PDF and ask to analyze the content. There is no note in our data about audio and video input, so please check this in the BotHub interface before a large task — files are attached directly to the message there.
The model data does not describe which languages it works with, so we will not promise anything specific. The most reliable way is to test it on your tasks: open the model in BotHub; access from Russia without a VPN or foreign card, payment in rubles based on actual token usage.