Gemini 4 Argon Batch
Neural Network
Gemini 4 Argon is Google's flagship reasoning model: complex development, math and science tasks, autonomous agents.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 943 718 tokens
- Reasoning effort
- 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 4 Argon Batch?
Gemini 4 Argon is Google's flagship reasoning model, trained to maintain context on long, multi-step tasks: complex development, deep mathematical and scientific calculations, and autonomous agents. The batch variant is designed for processing large sets of requests when real-time responses aren't required. In BotHub, the model is accessible from Russia without a VPN or foreign card: pay with a Russian card in rubles only for tokens used, which do not expire. Access over 250 neural networks in one window, easily compare answers, switch models, and use a unified OpenAI-compatible API to avoid rewriting integrations. Traffic is encrypted via AES-GCM, chats are not saved, and companies get contracts, invoices, electronic document management, and an admin panel with limits. Useful for designing or refactoring large services, finding floating bugs, solving problems with rigorous derivations, building agents that complete multi-step scenarios, or processing arrays of similar tasks.Frequently asked questions about Gemini 4 Argon Batch
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 context window of about a million tokens. It analyzes documents and images, writes and refactors code, and helps with debugging, analytics, and STEM tasks. It is suitable for scenarios requiring large amounts of material to be kept in memory and referenced in the response.
The output limit is up to 943,718 tokens per response, which is equivalent to a whole book or a large code module. In practice, it is better to limit the length yourself: request the desired format and size to keep the response structured and readable.
Yes, reasoning mode is supported. The model builds a solution path before the final answer, which significantly helps with math, logic puzzles, analyzing long documents, and finding code errors. For simple queries, this mode is not necessary — the answer will arrive faster.
Yes, the model works with function calling and structured JSON output. You describe the tool schema, it decides when to call them, and returns the answer in the specified format — convenient for agents, pipelines, and integration with your services.
It accepts text, images, and documents as input, including native file processing. You can send an interface screenshot, diagram, contract scan, or a large PDF and ask it to analyze the content, extract data, or provide a summary.
Google's Gemini family models work confidently with Russian queries: they understand phrasing, terms, and dialogue context. In BotHub, you access it via the website, Telegram bot, or a unified API — without a VPN or foreign card, paying in rubles based on actual usage.