Gemma 2 27B
Neural Network
Gemma 2 27B is Google's open language model for text tasks, available via a unified BotHub API.
Max answer length
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Context size
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Prompt cost
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Answer cost
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How it works Gemma 2 27B?
Gemma 2 27B is an open 27-billion parameter model from Google, built on the same research and technologies as the Gemini family. This is an instruction-tuned version focused on text-based tasks, making it ideal for predictable text processing. BotHub provides access without VPN or foreign cards: pay in rubles with a Russian card only for what you use, no subscription needed, and tokens don't expire. Requests go through a unified OpenAI-compatible API, allowing you to switch from Gemma 2 27B to any of over 250 other models without rewriting your integration, with traffic encrypted via AES-GCM. It's useful for drafting texts and newsletters, summarizing long documents, answering common chatbot questions, or prototyping services on an open model. For companies, BotHub provides contracts, invoices, and electronic document management, while the admin panel keeps team expenses under control.Frequently asked questions about Gemma 2 27B
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.
Gemma 2 27B it is an instruction-tuned text model from Google. It is suitable for correspondence, drafts, summaries, answering questions, basic coding tasks, and document analysis. You can input images. The context window is 8192 tokens, so it is more comfortable to work with short and medium-length materials.
The model generates up to 2048 tokens per response — enough for a short article, email, or document section. If you need longer text, break the task into parts and ask for a continuation in the next message, keeping in mind the 8192-token total window.
A separate reasoning mode is not specified in our data, so we cannot promise hidden chain-of-thought. However, you can explicitly ask it to explain the solution step-by-step — this technique helps with logical and educational tasks. For complex reasoning chains, it is more convenient to switch to a reasoning model.
Support for function calling and strict JSON mode is not specified in our data, so we cannot provide guarantees. You can describe the schema in the prompt and request the response in JSON only, but implement validation on your end. If tools are critical, BotHub's unified OpenAI-compatible API allows you to switch models without rewriting your integration.
Images and documents — yes, you can attach them to the request: the model will analyze a scan, table, or diagram and describe the content in text. Audio and video input is not specified in our data. Keep the 8192-token window in mind: it is better to split large files into parts.
There is no information in our data regarding prompt and response languages, so we cannot promise quality in advance. It is easier to check by practice: send your typical request and compare it with another model — switching in BotHub takes a couple of seconds, and payment is made in rubles based on usage.