Nemotron 3 Ultra

1M-token reasoning for coding agents and deep research.

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Nemotron 3 Ultra — Open Frontier-Reasoning LLM (Text Generation)

What is Nemotron 3 Ultra?

Nemotron 3 Ultra is NVIDIA's largest open-weight language model, built for long-running agentic workflows rather than single-turn chat. It is a 550-billion-parameter Mixture-of-Experts model that activates only 55 billion parameters per token, using a hybrid Mamba-Transformer architecture with LatentMoE routing and Multi-Token Prediction. On Segmind it runs as a synchronous text-to-text API: send a prompt, receive the generated text directly. It supports a context window of up to 1 million tokens, making it well suited to reasoning over large codebases, long documents, and multi-source research in a single request.

Key Features

  • •550B total / 55B active Mixture-of-Experts for frontier accuracy at high throughput
  • •Hybrid Mamba-2 plus Attention layers for efficient long-context handling and precise recall
  • •Up to 1M-token context window; leads open LLMs on RULER at 1M context length
  • •Controllable reasoning: reasoning on, off, and low-effort modes with an optional reasoning budget
  • •Strong tool calling, function calling, and structured multi-step planning for agent harnesses
  • •Multilingual output across English plus ten additional languages and 43 programming languages

Best Use Cases

Nemotron 3 Ultra shines as the planner or orchestrator inside an agent system. Reach for it on the genuinely hard calls: sustaining architectural decisions across long coding sessions, synthesizing contradictory evidence across hundreds of research sources, or verifying designs across thousands of constraints. It is a strong fit for coding agents, enterprise document RAG, deep research automation, long-document analysis, and multi-agent orchestration where accuracy and long-context reasoning matter more than raw speed. For simple chat or high-volume routine calls, a smaller model is usually the better choice.

Prompt Tips and Output Quality

Because the model first generates a reasoning trace and then a final answer, give it a clear goal, explicit structure, and any constraints up front. Ask for numbered steps or headed sections when you want a structured report. For high-volume steps, prefer low-effort reasoning to cut tokens; reserve full reasoning for complex planning. If you need strict JSON or schema output, validate and retry inside your harness, as structured-output reliability can vary.

FAQs

Is Nemotron 3 Ultra open weights? Yes. NVIDIA released the weights, training recipes, and datasets under a permissive open model license.

What context length does it support? Up to 1 million tokens, with strong performance on long-context retrieval benchmarks.

Is it good for coding agents? Yes. It is post-trained for agent harnesses and performs well on SWE-bench and Terminal-Bench style tasks with efficient token usage.

How does it compare to other open models? It is the leading US open-weight model on the Artificial Analysis Intelligence Index, trailing the Chinese-led open frontier such as Kimi K2.6 on raw intelligence while leading on throughput.

Can it call tools? Yes. It supports streaming reasoning and incremental tool and function calls, designed for multi-turn agent loops.

What are good alternatives? Nemotron 3 Super or Nano for cheaper, faster steps, and Kimi K2.6 or Qwen 3.5 when maximum benchmark intelligence is the priority.