NVIDIA Nemotron 3 Ultra
NVIDIA's flagship open-weights frontier model — a 550B-parameter hybrid Mamba-MoE architecture built for agentic reasoning, tool use, and long-context throughput.
Overview
What is NVIDIA Nemotron 3 Ultra
NVIDIA Nemotron 3 Ultra is a frontier-scale 550B-parameter (55B active) open-weights language model released in June 2026. It combines a hybrid Mamba-2, MoE, and Attention architecture with Multi-Token Prediction for efficient high-throughput agentic reasoning, native tool use, and long-context analysis.
Running it privately on Venice
On Venice, Nemotron 3 Ultra runs privately with zero retention of your prompts — no surveillance, no training data harvesting. You get native tool use, reasoning, and web search capabilities with the efficiency of an open-weights model, processed on Venice's permissionless infrastructure without a Big Tech account.
Assessment
Strengths and limitations
- Open weights under OpenMDW-1.1: full checkpoints, training data, and recipes released for self-hosting and fine-tuning.
- Frontier agentic performance with native tool use, reasoning, and web search support for complex multi-step workflows.
- Hybrid Mamba-2 + MoE + Attention architecture with Multi-Token Prediction delivers up to ~6× higher inference throughput than comparable open LLMs.
- Configurable reasoning budget via chat template (enable_thinking), letting you trade speed for depth per task.
- Strong multilingual coverage across English, French, Spanish, Italian, German, Japanese, Korean, Hindi, Brazilian Portuguese, and Chinese.
- Designed for up to 1M token context length and strong long-context RULER performance, with 256K available on Venice.
- Not uncensored: it carries safety alignment and will refuse certain requests, unlike fully uncensored models.
- Massive hardware footprint for self-hosting: BF16 requires 8× GB200/B200 or 16× H100, making local inference expensive.
- Venice currently offers a 256K context window and fp8 quantization, which is below the native 1M context and BF16 precision.
- Premium output pricing ($3.13/1M tokens) is higher than efficient rivals like DeepSeek V3.2.
- As a cutting-edge MoE architecture, it may have less third-party tooling and framework support than standard dense transformers.
Capabilities
What it supports
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
Specifications
Datasheet
- Maker
- NVIDIA Corporation
- Released
- June 4, 2026
- Architecture
- LatentMoE — Mamba-2 + MoE + Attention hybrid with Multi-Token Prediction (MTP)
- Parameters
- 550B total / 55B active
- Open weights
- Yes — OpenMDW-1.1 license
- Context window
- 256K tokens
- Max output
- 32.768K tokens
- Capabilities
- Function calling, Reasoning, Web search
- Privacy on Venice
- Private — zero retention
- Available on Venice since
- Jun 2026
API
Call it from your code
Venice exposes an OpenAI-compatible API. Point your base URL at Venice and pass the model id.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "nvidia-nemotron-3-ultra-550b-a55b",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Pricing
What it costs on Venice
Billed per token on Venice: $0.63 per 1M input tokens and $3.13 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
Alternatives
How it compares
| Model | Price (Venice) | Context window | Open weights | Best for |
|---|---|---|---|---|
| NVIDIA Nemotron 3 Ultra | $0.63 in · $3.13 out / 1M | 256K tokens | Yes | Agentic reasoning & throughput |
| Kimi K2.6 | $0.75 in · $3.50 out / 1M | 256K tokens | Yes | Long-context reasoning & coding |
| DeepSeek V3.2 | $0.33 in · $0.48 out / 1M | 160K tokens | Yes | Efficiency & math/code |
| Claude Sonnet 4.6 | $3.60 in · $18 out / 1M | 1M tokens | No | General-purpose closed model |
NVIDIA's most capable open-weights model — hybrid Mamba-MoE with configurable reasoning and native tool use.
Use cases
What it is good for
- 01Long-running autonomous agents that plan, call tools, and maintain state across many turns.
- 02High-stakes RAG over very long documents and legal or scientific corpora.
- 03Multilingual reasoning and analysis across code, math, and science benchmarks.
- 04Enterprise workflows requiring open weights and data sovereignty for compliance.
- 05Code generation and review with extended context for large repositories.
Prompting
Getting better results
Toggle reasoning depth with the chat template (enable_thinking=True/False) depending on whether you need deep analysis or fast answers.
Leverage the 256K context window for large document RAG — feed full papers or codebases in a single prompt.
Use structured output (JSON schema) when building agents that need machine-readable tool arguments.
For multi-turn agent tasks, keep the conversation history concise to maximize available context for reasoning.
FAQ
Frequently asked questions
NVIDIA Nemotron 3 Ultra is a 550B-parameter (55B active) open-weights frontier language model released in June 2026. It uses a hybrid Mamba-2, MoE, and Attention architecture with Multi-Token Prediction for high-throughput agentic reasoning, tool use, and long-context analysis.
Venice charges $0.63 per 1M input tokens and $3.13 per 1M output tokens. Cached input is $0.19 per 1M tokens. There is no subscription required — you pay per token with credits.
You can try it free on Venice — new accounts receive welcome credits and a daily free tier, with no credit card required. Continued use is billed per token in credits.
It is open weights under the OpenMDW-1.1 license. NVIDIA releases the base, post-trained, and quantized checkpoints along with training data and recipes, but it is not an open-source code project in the traditional sense.
Yes. On Venice it supports function calling, reasoning, and web search, plus structured JSON schema output for building agents and automated workflows.
Nemotron 3 Ultra emphasizes agentic throughput and configurable reasoning with a hybrid Mamba-MoE architecture. Kimi K2.6 is also open weights with strong long-context and coding skills. Choose Nemotron for complex multi-step agents and Kimi for deep coding tasks.
Venice currently hosts Nemotron 3 Ultra with a 256K token context window and up to 32,768 tokens max output. NVIDIA's native model supports up to 1M tokens for deployments with sufficient hardware.
Yes. The model supports a configurable reasoning mode via the chat template (enable_thinking=True/False), letting you control the depth of analysis and token usage per request.
No. It is a safety-aligned model and will refuse certain harmful or restricted requests. It is not uncensored, though Venice runs it privately without content monitoring or logging.
Run NVIDIA Nemotron 3 Ultra privately
No prompt logging. No data used for training.