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More neural networks on Bothub

Claude Opus 5.5
Claude Opus 5.5
Anthropic’s flagship model for complex reasoning, programming and long-running agentic tasks. It continues the Claude Opus 5 line and is especially strong at multi-step changes in large codebases. Works with text, images and documents, supports function calling and a context of up to 1M tokens.
Nano Banana 2
Nano Banana 2
Google model for image generation and editing—a more affordable Pro alternative. Up to 4K, 5 characters and 14 objects preserved, crisp text in any language. 4× faster and cheaper than Pro.
Kling 3 Video
Kling 3 Video
Kuaishou flagship for videos up to 15 seconds with native sound and dialogue. Up to 4K, realistic physics, frame control, multi-shot with consistent characters and Director Mode.
GPT 6 Sol
GPT 6 Sol
An OpenAI model from the GPT-6 line, positioned between the flagship Astra and the fast Luna. Built for demanding professional tasks — large documents, code and analytics — while staying cost-efficient. Supports reasoning, tools, image and document input, and a context of up to 1.05M tokens.
Flux 2 max
Flux 2 max
Black Forest Labs flagship model with 32B parameters. Photorealistic image generation up to 4 MP, improved typography and support for up to 10 references for consistent character and object editing. Precise color control via HEX codes, Grounding Search mode for visualizing current events and products, and best-in-line prompt adherence.
Claude Sonnet 5.5
Claude Sonnet 5.5
An Anthropic Sonnet-class model for everyday work: building features, fixing bugs, code review and refactoring. The successor to Claude Sonnet 5 with stronger results on engineering tasks. Supports reasoning, function calling and a context of up to 1M tokens.
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What are Caps?

Caps is the internal currency of the service, used to measure the cost of requests and responses of neural networks. It is fixed and depends on the model complexity: number of parameters, multimodality, and overall power.

    For example:
  • ChatGPT-3.5 — ~1 Caps per token
  • ChatGPT o1-Pro — ~400+ Caps per token
The higher your tariff, the better the price: 1 million Caps is cheaper on Elite than on Basic.

Still have questions?

What are tokens?

Tokens are units of text processing by the neural network, representing parts of words, entire words, or punctuation marks that determine the cost of requests.

How long will 1 million tokens last?

One million tokens of the GPT-4o model are enough to rewrite “The Brothers Karamazov” by F. M. Dostoevsky.

What to do if I run out of tokens?

Purchase additional Caps in your personal account — https://bothub.chat/profile

Why does the neural network pretend to be another?

The neural network does not know what model it is if it is not specified in the system prompt. The “self-identification” of the model without instruction is influenced by many factors, one of them being the model's data training set.

What is context in a neural network?

Context is the amount of information that the neural network retains in memory during a dialogue, affecting the coherence of responses and understanding of previous requests.

What is the context of different neural network models?

GPT o1 Pro and Claude 3.7 Sonnet support up to 200K tokens, Gemini 2.5 Pro works with 1KK, while Gemini 2.0 Pro supports up to 2KK tokens.

What file formats do models read?

Neural networks process TXT, PDF, DOCX, XLSX, CSV, JSON, XML, HTML, as well as images JPG, PNG, and audio files MP3, MP4.

Can neural networks be used for free?

There are free models with the postfix “:free” and “-exp” that can be used for free through a mini-window on the main page, as well as the model page.

How do neural network models differ from each other?

Models differ in the volume of training data, context size, processing speed, specialization in specific tasks, and ability to work with multimodal content.

How to use models via API?

To integrate models into your applications, you need to obtain an API key in your personal account. More details can be found here: https://bothub.chat/api/documentation/ru.

Can neural networks be used to automate business processes?

Neural networks effectively automate routine tasks of document management, data processing, customer support, and analytics, integrating with existing business systems via API.

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