How it works Gemma 4 26B A4B IT?
Gemma 4 26B A4B IT is an instruction-tuned Google DeepMind model with a Mixture-of-Experts architecture: out of 25.2B parameters, only 3.8B are activated per token, making its quality comparable to dense models of around 31B parameters while remaining computationally efficient. Instruction tuning means the model is optimized for task execution and dialogue, rather than simple text completion. In BotHub, it works from Russia without a VPN or foreign card; payment is made with Russian cards in rubles, pay-as-you-go per token, no subscription required. Over 250 models are available in one window: compare responses and switch to another neural network without rewriting your integration, as the API is unified and OpenAI-compatible. Chats are encrypted via AES-GCM, and companies have access to contracts, invoices, electronic document management, and an admin panel with limits. Useful for summarizing long correspondence, drafting emails or product descriptions, running batches of similar requests via API, building a chat assistant prototype, or testing a hypothesis on a lightweight model before deploying on a heavier one.Frequently asked questions about Gemma 4 26B A4B IT
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 text model from Google with support for reasoning, tool calling, and accepting images and documents. It is suitable for writing and refactoring code, debugging, analyzing large materials, STEM tasks, and working with long documents in a context of up to 262,144 tokens.
The output limit is up to 32,768 tokens per response. This is enough for a voluminous article, detailed code analysis, a large module, or a structured report. If the material is even longer, break the task into parts and continue generation in the same dialogue.
Yes, reasoning mode is supported: the model breaks down the task step-by-step before providing a result. This significantly helps in mathematics, logic, data analysis, and debugging code, where the sequence of intermediate conclusions is more important than a quick response.
Yes, the model supports function calling and structured JSON output. It is easy to integrate into agents, chatbots, and pipelines via the unified OpenAI-compatible BotHub API: there is no need to change the integration scheme, just specify a different model identifier.
Text, images, and documents are accepted as input: you can ask it to analyze a diagram, interface screenshot, table, or PDF. Audio and video support is not noted in our data, so for such formats, choose a specialized model in the same BotHub window.
The model belongs to the multilingual Gemma family, so for Russian-language tasks, it is worth testing it with your real prompts. In BotHub, this is inexpensive: payment is pay-as-you-go per token, and if necessary, you can switch to another model without changing the integration.