Hermes 4 405B
Neural Network
Hermes 4 405B is a Nous Research reasoning model based on Llama 3.1 405B for complex multi-step tasks.
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How it works Hermes 4 405B?
Hermes 4 405B is a large reasoning model from Nous Research based on the Llama 3.1 architecture with 405 billion parameters. Its key feature is a hybrid mode: the model can deliberate on a task using internal reasoning before providing an answer, or respond immediately when a detailed breakdown isn't needed. This is helpful where logical chains are critical: multi-step tasks, hypothesis testing, and working with code or technical texts. In BotHub, the model is accessible from Russia without a VPN or foreign card, and you pay in rubles based on actual usage—per token, with no subscription. Over 250 models are available in the same window, allowing easy switching and comparison of responses to a single prompt, while a unified OpenAI-compatible API eliminates the need to rewrite integrations when switching models; chats are encrypted. Developers will find Hermes 4 useful for code generation, refactoring, debugging, and analyzing existing solutions. Analysts can use it to untangle calculation logic and gather arguments. Engineers and students can use it for math and science problems. Technical writers can use it for documentation drafts and structuring long explanations.Frequently asked questions about Hermes 4 405B
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 large open model from Nous Research with a 131,072 token context window. It is suitable for long texts and documents, analytics, code and refactoring, STEM tasks and logic, as well as agentic scenarios with tool calling. In BotHub, it is available from Russia without a VPN or foreign card.
The output limit is up to 117,964 tokens per response, covering almost the entire 131,072 token context window. This is enough for a voluminous technical document, a large code module, or a detailed breakdown with a long chain of reasoning without splitting into parts.
Yes, the reasoning mode is confirmed: the model can unfold a chain of thought before the final answer. This is useful in mathematics, logic problems, code debugging, and analyzing complex documents where the path to the conclusion is as important as the final formulation.
Yes, the model supports function calling and structured JSON output. It is easy to integrate into agents, pipelines, and business logic: it returns predictable structures and calls external tools. In BotHub, this works via a unified OpenAI-compatible API.
In addition to text, images and documents are accepted as input: you can send a screenshot, diagram, PDF, or contract and ask for an analysis, summary, or data extraction. Audio and video are not supported in our data, and you receive text as output.
The 405-billion parameter model works confidently with Russian-language queries and documents, although we do not formally publish language benchmarks. We recommend testing it on your tasks, and if necessary, switching to another model in BotHub without editing your integration.