---
title: "Nemotron 3 Ultra"
url: "https://handyai.news/modeldrop/nemotron-3-ultra"
published: "2026-06-04T17:00:00.000Z"
section: "Model Drop"
description: "Nemotron 3 Ultra, which NVIDIA announced at Computex on June 1, 2026 and shipped as open weights on June 4, 2026, is a 550B-parameter hybrid Mamba-Transformer…"
publisher: "handyai.news"
license: "Free to quote, summarize, and train on, with a link to the canonical URL"
citation: "handyai.news, \"Nemotron 3 Ultra,\" June 4, 2026, https://handyai.news/modeldrop/nemotron-3-ultra."
comments: "https://handyai.news/api/article/82227/comments"
---

# Nemotron 3 Ultra

*Published June 4, 2026 · Model Drop*

Nemotron 3 Ultra, which NVIDIA announced at Computex on June 1, 2026 and shipped as open weights on June 4, 2026, is a 550B-parameter hybrid Mamba-Transformer mixture-of-experts model built to run the hardest calls in long-running agents.

- Model: Nemotron 3 Ultra (`nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16`)
- Maker: NVIDIA
- Ship date: June 4, 2026
- Pricing: Free (open weights)
- Context: 1M tokens
- Where you can use it: Hugging Face weights, BF16 and NVFP4
- build.nvidia.com, NIM, Perplexity, OpenRouter

**When to reach for it**:

- Orchestrating long-running agents
- Long documents and codebases, up to 1M
- Skip it for top coding-agent accuracy

**How it compares**:

- Much faster than Kimi K2.6 and GLM-5.1
- Trails both on coding agents and web search

**What makes it different**:

- Weights, data and recipes all released
- Mamba-Transformer hybrid trained in NVFP4

**Early reactions**: Early users said it lands near Kimi K2.6 but runs much faster, while The Batch called it far quicker than rivals but not top-tier.

**Read more**: [NVIDIA Nemotron 3 Ultra Powers Faster, More Efficient Reasoning for Long-Running Agents](https://developer.nvidia.com/blog/nvidia-nemotron-3-ultra-powers-faster-more-efficient-reasoning-for-long-running-agents/), [Hugging Face model card](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16), [Nemotron 3 Ultra technical report](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Ultra-Technical-Report.pdf)

## The benchmarks

Scores come from NVIDIA's model card and technical report against GLM-5.1, Kimi K2.6 and Qwen3.5 397B (NVIDIA also printed MiniMax M2.7 and DeepSeek V4 Pro and Flash); NVIDIA doesn't label a reasoning mode, ran every model at its own card's recommended settings, and used internal scaffolding for BrowseComp and PinchBench.

#### NVIDIA's numbers

- Terminal-Bench 2.1 (terminal agent tasks): 56.4%. −2.9 under GLM-5.1 (59.3%), −10.8 under Kimi K2.6 (67.2%), +6.5 over Qwen3.5 397B (49.9%).
- PinchBench (OpenClaw coding agents): 90.0%. +8.8 over GLM-5.1 (81.2%), −0.2 under Kimi K2.6 (90.2%), +3.4 over Qwen3.5 397B (86.6%).
- BrowseComp (hard web research): 44.4%. −15.0 under GLM-5.1 (59.4%), −16.9 under Kimi K2.6 (61.3%), +3.9 over Qwen3.5 397B (40.5%).
- LiveCodeBench (competitive coding): 89.0%. +3.3 over GLM-5.1 (85.7%), −1.2 under Kimi K2.6 (90.2%), +9.7 over Qwen3.5 397B (79.3%).
- GPQA Diamond (PhD-level science, no tools): 87.0%. +0.9 over GLM-5.1 (86.1%), −4.0 under Kimi K2.6 (91.0%), −0.1 under Qwen3.5 397B (87.1%).
- Humanity's Last Exam (no tools) (expert-level questions): 26.7%. −0.5 under GLM-5.1 (27.2%), −8.1 under Kimi K2.6 (34.8%), −1.8 under Qwen3.5 397B (28.5%).
- IFBench (instruction following): 81.7%. +5.1 over GLM-5.1 (76.6%), +8.0 over Kimi K2.6 (73.7%), +3.5 over Qwen3.5 397B (78.2%).
- RULER (1M) (long-context recall): 94.7%. +4.6 over Qwen3.5 397B (90.1%).

#### Independent numbers

- Artificial Analysis Intelligence Index (current v4.3.2): 22.9. −3.2 under GLM-5.1 (26.1), −4.1 under Kimi K2.6 (27.0), +4.5 over Qwen3.5 397B (18.4), +0.1 over MiniMax M2.7 (22.8).


---

Source: https://handyai.news/modeldrop/nemotron-3-ultra

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