---
title: "Nemotron 3 Super"
url: "https://handyai.news/modeldrop/nemotron-3-super"
published: "2026-03-11T17:00:00.000Z"
section: "Model Drop"
description: "Nemotron 3 Super, which NVIDIA shipped on March 11, 2026, is an open-weight hybrid Mamba-Transformer mixture-of-experts model with 120B total and 12B active…"
publisher: "handyai.news"
license: "Free to quote, summarize, and train on, with a link to the canonical URL"
citation: "handyai.news, \"Nemotron 3 Super,\" March 11, 2026, https://handyai.news/modeldrop/nemotron-3-super."
comments: "https://handyai.news/api/article/82226/comments"
---

# Nemotron 3 Super

*Published March 11, 2026 · Model Drop*

Nemotron 3 Super, which NVIDIA shipped on March 11, 2026, is an open-weight hybrid Mamba-Transformer mixture-of-experts model with 120B total and 12B active parameters and a 1M-token context, built for multi-agent workloads.

- Model: Nemotron 3 Super (`nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16`)
- Maker: NVIDIA
- Ship date: March 11, 2026
- Pricing: Free (open weights)
- Context: 1M tokens
- Where you can use it: Hugging Face in BF16, FP8 and NVFP4
- build.nvidia.com, Perplexity and OpenRouter

**When to reach for it**:

- Multi-agent apps with long, tool-heavy runs
- Skip it if top coding scores matter most

**How it compares**:

- Faster than gpt-oss-120b, per NVIDIA
- Trails Qwen3.5-122B-A10B on most evals

**What makes it different**:

- Mamba-Transformer hybrid with latent MoE
- Pretrained in 4-bit NVFP4, with MTP heads

**Early reactions**: Launch-day testers at Kilo called it a strong workhorse for scaffolding projects and summarizing docs, but found it weak with image-generation tools.

**Read more**: [New NVIDIA Nemotron 3 Super Delivers 5x Higher Throughput for Agentic AI](https://blogs.nvidia.com/blog/nemotron-3-super-agentic-ai/), [Introducing Nemotron 3 Super](https://developer.nvidia.com/blog/introducing-nemotron-3-super-an-open-hybrid-mamba-transformer-moe-for-agentic-reasoning/), [Model card](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16), [Technical report](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Super-Technical-Report.pdf)

## The benchmarks

Scores are NVIDIA's BF16 checkpoint with reasoning on, against Qwen3.5-122B-A10B and gpt-oss-120b; NVIDIA used official rival scores where it could and filled the rest from aggregators or its own runs, and it ran SWE-bench Verified (OpenHands) and Terminal-Bench 2.0 on its own or official harnesses.

#### NVIDIA's numbers

- SWE-bench Verified (real GitHub fixes, OpenHands): 60.47%. −5.93 under Qwen3.5-122B-A10B (66.40%), +18.57 over gpt-oss-120b (41.9%).
- Terminal-Bench 2.0 (agentic terminal tasks): 31.00%. −6.50 under Qwen3.5-122B-A10B (37.50%), +12.30 over gpt-oss-120b (18.70%).
- τ²-bench Telecom (customer-support tool use): 64.36%. −30.64 under Qwen3.5-122B-A10B (95.00%), −1.64 under gpt-oss-120b (66.00%).
- RULER @ 1M (1M-token long context): 91.75%. +0.42 over Qwen3.5-122B-A10B (91.33%), +69.45 over gpt-oss-120b (22.30%).
- HMMT Feb25 (competition math, no tools): 93.67%. +2.27 over Qwen3.5-122B-A10B (91.40%), +3.67 over gpt-oss-120b (90.00%).
- GPQA Diamond (grad-level science, no tools): 79.23%. −7.37 under Qwen3.5-122B-A10B (86.60%), −0.87 under gpt-oss-120b (80.10%).
- Humanity's Last Exam (no tools) (expert-level questions): 18.26%. −7.04 under Qwen3.5-122B-A10B (25.30%), +3.36 over gpt-oss-120b (14.90%).
- IFBench (instruction following): 72.56%. −1.21 under Qwen3.5-122B-A10B (73.77%), +4.24 over gpt-oss-120b (68.32%).

#### Independent numbers

- Artificial Analysis Intelligence Index (current v4.3.2): 12.8. +1.2 over gpt-oss-120b (11.6), −2.8 under Qwen3.5-122B-A10B (15.6).


---

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

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