Nemotron 3 Ultra 550B
Neural Network
Nemotron 3 Ultra is an open NVIDIA model for reasoning and orchestration, available via API in the unified BotHub interface.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 000 000 tokens
- Max answer length
- 65 536 tokens
- Reasoning effort
- none, medium, high
- Capabilities
- reasoning, function calling
How it works Nemotron 3 Ultra 550B?
Nemotron 3 Ultra is an open NVIDIA model based on a hybrid Transformer-Mamba architecture with a Mixture of Experts: out of 550 billion parameters, about 55 billion are active at each step. The model's profile is reasoning and orchestration: step-by-step task breakdown and management of other agents. You can connect to it in BotHub without a VPN or foreign card: pay with Russian cards in rubles, pay-as-you-go, tokens do not expire. Over 250 models are available in one window, and a unified OpenAI-compatible API allows you to replace the model in a multi-model system without rewriting the integration. Chats are encrypted via AES-GCM, and teams have access to contracts, invoices, electronic document management, and an admin panel with limits. Engineers use this model as the core of an agent pipeline: it plans steps and distributes subtasks among faster models. It helps analysts build long chains of reasoning and verify their logic. It is also useful for step-by-step STEM problem solving, and open weights simplify prototyping scenarios for deployment in your own infrastructure.Frequently asked questions about Nemotron 3 Ultra 550B
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 an NVIDIA text model for complex tasks: code analysis, refactoring and debugging, mathematics and STEM, and working with long documents. A context of up to one million tokens allows you to keep an entire project or set of instructions in one dialogue, and function calling connects the model to your services.
In a single response, the model outputs up to 65,536 tokens — that's dozens of pages of text. Enough for a large code module, a detailed technical analysis, or an entire long document. If the task is even larger, break it into parts: the one-million-token context will retain everything written previously.
Yes, the reasoning mode is confirmed in the specifications: before answering, the model builds a chain of steps, checks intermediate conclusions, and only then formulates the result. This is noticeable in mathematics, logic, and debugging, where it is important not to guess the answer, but to reach it sequentially.
Yes, the model supports function calling and structured JSON output, making it convenient to use as the basis for agents and pipelines. In BotHub, this works via a unified OpenAI-compatible API: you describe the tool schema once, and you can change the model if necessary without rewriting the integration.
Images and documents — yes, they can be sent as input along with text: you can ask it to analyze a diagram, interface screenshot, table, or multi-page file. There is no mention of audio or video in our data, so do not count on them in advance. The model returns the response in text.
There are no separate measurements for the Russian language in our data, so we will not promise accuracy — check it on your own task, it's faster than any benchmarks. BotHub itself is Russian-speaking: interface, support, and payment with Russian cards, access without VPN or foreign cards.