Gemini 2.5 Flash
Neural Network
Gemini 2.5 Flash is a Google model with a built-in reasoning engine for code, math, and scientific tasks.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
Image prompt
(per 1K tokens)
How it works Gemini 2.5 Flash?
Gemini 2.5 Flash is a Google model designed for advanced reasoning, programming, math, and scientific tasks. It features a built-in reasoning engine: the model processes the task before answering, handling multi-step reasoning more reliably than direct generation. Through BotHub, you can connect without a VPN or foreign card, pay with a Russian card only for tokens used, and tokens do not expire. In the same window, you have access to over 250 other neural networks, making it easy to switch and compare answers. A unified OpenAI-compatible API allows you to change models in a multi-model system without rewriting integrations. Data is encrypted via AES-GCM and not stored. For companies, we offer contracts, invoices, electronic document management, and an admin panel with limits. Developers can use Gemini 2.5 Flash for writing and refactoring code, debugging, and error analysis. Analysts can use it to break down calculations and verify logic. Students and researchers can use it for math and science problems. Product teams can use it as the core of an assistant that maintains long chains of reasoning.Frequently asked questions about Gemini 2.5 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.
The model works with text, code, and documents: it parses large files, answers based on their content, and helps with refactoring and debugging. The context window is 1,048,576 tokens, so you can upload an entire project or a large report and discuss it in a single chat.
In a single response, the model outputs up to 65,535 tokens — enough for a long article, detailed documentation, or a large code file. If you need more volume, break the task into parts and continue in the same chat: the context allows it.
Yes, reasoning before answering is confirmed: the model builds a chain of thought, making it more stable in multi-step tasks like math, logic, and code analysis. On simple queries, the difference is barely noticeable, but on complex ones, the output accuracy is significantly higher.
Yes, both function calling and structured JSON output are supported. You can connect external tools, build agents, and get responses strictly according to a specified schema. All this works via the unified OpenAI-compatible BotHub API, without rewriting integrations when switching models.
Images and documents — yes, including native file processing: you can send a scan, PDF, or image and ask questions about the content. Audio and video input is not explicitly noted in our data, but that doesn't mean it's impossible — it's easier to test with your own file.
The language of prompts and output is not specified in our data, so we won't make promises: evaluate the model with your own text. In BotHub, it is available from Russia without a VPN or foreign card, with payment in rubles and no subscription — tokens do not expire.