NVIDIA: Nemotron 3 Ultra

NVIDIA Nemotron 3 Ultra is an open frontier reasoning and orchestration model from NVIDIA. It is a mixture of experts model with 55B active parameters out of 550B total, built on a hybrid Transformer-Mamba architecture. The model id on api14 is nvidia/nemotron-3-ultra-550b-a55b, and it sits in the text category, so it is aimed at language work rather than image, audio or video generation.

The model takes text as input and returns text as output. It has a context length of 262144 tokens, which is enough to hold long documents, large code files or extended conversation histories in a single request, and a maximum output of 182520 tokens, so long generations are possible without splitting the work across calls. Reasoning behaviour is exposed through the include_reasoning and reasoning parameters, and reasoning_effort lets you control how much internal deliberation the model applies before answering. Tool calling is supported through tools and tool_choice, and structured_outputs plus response_format let you constrain the shape of the returned text.

Those capabilities point to a few practical uses. Long context plus reasoning suits analysis of large codebases, multi document review, and research tasks where the model has to hold many sources at once and reconcile them. Tool calling makes it a reasonable fit for agent style workflows and orchestration, where the model decides which functions to invoke and in what order. Structured outputs are useful when the result has to feed directly into another system, such as extracting fields from contracts or producing machine readable summaries. The large output ceiling helps for tasks that produce long artefacts, including detailed reports or extended code generation.

You call the model through api14's OpenAI-compatible API using the model id nvidia/nemotron-3-ultra-550b-a55b. Requests accept the usual sampling and control parameters: temperature, top_p, top_k, min_p, frequency_penalty, presence_penalty and repetition_penalty for shaping output, max_tokens for length, seed for reproducibility, stop for stopping sequences, and logit_bias for token level adjustments. For tool use, pass tools and tool_choice. For structured results, use response_format and structured_outputs. Reasoning is controlled with reasoning, reasoning_effort and include_reasoning. Because the interface is OpenAI-compatible, existing clients and SDKs generally work with only the base URL and model id changed.

One thing to keep in mind is that the model is text only. If your task depends on images, audio or other input modalities, this model will not handle it, and a multimodal option would be a better fit. The very large context and output limits also mean requests can be heavy, so for short, simple prompts a smaller model may respond faster and be easier to manage. Within text work, though, the combination of long context, reasoning controls, tool calling and structured outputs covers a wide range of developer needs.