---
title: "Nemotron 3 Nano"
url: "https://handyai.news/modeldrop/nemotron-3-nano"
published: "2025-12-15T17:00:00.000Z"
section: "Model Drop"
description: "Nemotron 3 Nano, which NVIDIA shipped on December 15, 2025, is a 30B open-weight hybrid Mamba-Transformer mixture-of-experts model with 3.5B active…"
publisher: "handyai.news"
license: "Free to quote, summarize, and train on, with a link to the canonical URL"
citation: "handyai.news, \"Nemotron 3 Nano,\" December 15, 2025, https://handyai.news/modeldrop/nemotron-3-nano."
comments: "https://handyai.news/api/article/82225/comments"
---

# Nemotron 3 Nano

*Published December 15, 2025 · Model Drop*

Nemotron 3 Nano, which NVIDIA shipped on December 15, 2025, is a 30B open-weight hybrid Mamba-Transformer mixture-of-experts model with 3.5B active parameters, and the first model of the Nemotron 3 family.

- Model: Nemotron 3 Nano (`nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16`)
- Maker: NVIDIA
- Ship date: December 15, 2025
- Pricing: Free (open weights)
- Context: 1M tokens (256K in the default config)
- Where you can use it: Hugging Face open weights
- build.nvidia.com, NIM and hosts like OpenRouter

**When to reach for it**:

- Many parallel agents on one GPU
- Long codebases and document piles
- Skip it if you need image or audio input

**How it compares**:

- More throughput than gpt-oss-20b, per NVIDIA
- On par with Qwen3-30B-A3B-Thinking-2507

**What makes it different**:

- Mamba-2 layers mixed with MoE and attention
- Open weights, data, recipes and RL gyms
- Reasoning on/off plus a thinking budget

**Early reactions**: An early hands-on test from Ori on December 17 praised its generation speed, though it still flubbed a simple letter-counting prompt.

**Read more**: [NVIDIA Debuts Nemotron 3 Family of Open Models](https://nvidianews.nvidia.com/news/nvidia-debuts-nemotron-3-family-of-open-models), [Inside NVIDIA Nemotron 3](https://developer.nvidia.com/blog/inside-nvidia-nemotron-3-techniques-tools-and-data-that-make-it-efficient-and-accurate/), [Hugging Face model card](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16), [Nemotron 3 Nano technical report](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf)

## The benchmarks

Scores are from NVIDIA's model card table against Qwen3-30B-A3B-Thinking-2507 and gpt-oss-20b; NVIDIA says RULER ran with reasoning off and AA-LCR with reasoning on, and doesn't label the rest (reasoning is on by default). NVIDIA took rival scores from official reports or Artificial Analysis and measured some itself.

#### NVIDIA's numbers

- AIME 2025 (competition math, no tools): 89.1%. +4.1 over Qwen3-30B-A3B-Thinking-2507 (85.0%), −2.6 under gpt-oss-20b (91.7%).
- LiveCodeBench (fresh coding problems, v6): 68.3%. +2.3 over Qwen3-30B-A3B-Thinking-2507 (66.0%), +7.3 over gpt-oss-20b (61.0%).
- Humanity's Last Exam (no tools) (expert-level questions): 10.6%. +0.8 over Qwen3-30B-A3B-Thinking-2507 (9.8%), −0.3 under gpt-oss-20b (10.9%).
- SWE-Bench (OpenHands) (real GitHub fixes): 38.8%. +16.8 over Qwen3-30B-A3B-Thinking-2507 (22.0%), +4.8 over gpt-oss-20b (34.0%).
- τ²-bench Telecom (customer-support tool use): 42.2%. +15.9 over Qwen3-30B-A3B-Thinking-2507 (26.3%), −7.5 under gpt-oss-20b (49.7%).
- IFBench (instruction following): 71.5%. +20.5 over Qwen3-30B-A3B-Thinking-2507 (51.0%), +6.5 over gpt-oss-20b (65.0%).
- RULER-100@1M (long-context retrieval, 1M tokens): 86.3%. +8.8 over Qwen3-30B-A3B-Thinking-2507 (77.5%).
- AA-LCR (long-document reasoning): 35.9%. −23.1 under Qwen3-30B-A3B-Thinking-2507 (59.0%), +1.9 over gpt-oss-20b (34.0%).

#### Independent numbers

- Artificial Analysis Intelligence Index (current v4.3.2): 8.9. −0.9 under Qwen3-30B-A3B-Thinking-2507 (9.8), −0.1 under gpt-oss-20b (9.0).


---

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

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