Nemotron 3 Nano Omni vs Llama 4 Scout
Nemotron 3 Nano Omni 30B-A3B-Reasoning (NVIDIA, April 28, 2026) and Llama 4 Scout (Meta, April 2025) are the two open-weight mid-tier models worth choosing between for self-hosted multimodal workloads. Nemotron processes text, image, video, and audio in one unified sequence across a 256K context window using a hybrid Mamba-Transformer-MoE backbone with 3 billion of 30 billion parameters active per token. It tops six public leaderboards spanning document intelligence, video understanding, and voice interaction. Llama 4 Scout brings the headline 10 million token context window and accepts text, vision, and code, but does not handle native audio or video. The choice usually comes down to which modality matters more: extreme context length, or end-to-end multimodal coverage.
Head-to-Head Specs
| Spec | Nemotron 3 Nano Omni | Llama 4 Scout |
|---|---|---|
| Provider | NVIDIA | Meta |
| Input Price | Free/1M | Free/1M |
| Output Price | Free/1M | Free/1M |
| Context Window | 256K | 10M |
| Released | 2026-04 | 2025-04 |
| Capabilities | text, vision, audio, video, code, reasoning, tool-use | text, vision, code |
Category Breakdown
Nemotron handles text, image, video, and audio natively in one model. Llama 4 Scout is text plus vision only.
Llama 4 Scout ships a 10,000,000 token context window vs Nemotron at 256,000. Roughly 40x more raw context.
Nemotron scored 65.8 on OCRBenchV2-En, best-in-class. Llama 4 Scout has no published score on this suite.
Nemotron scored 72.2 on Video-MME. Llama 4 Scout has no native video pathway.
Nemotron scored 89.4 on VoiceBench, best-in-class. Llama 4 Scout does not handle audio input.
Nemotron scored 47.4 on OSWorld, best-in-class for open multimodal models at this tier.
Both use sparse activation. Nemotron activates 3B of 30B; Llama 4 Scout uses an MoE pattern. Different tradeoffs, comparable inference economics.
Nemotron ships NVFP4 quantization that runs on a 24GB consumer GPU. Llama 4 Scout typically needs more aggressive quantization or multi-GPU for self-host.
Both are open weights with no per-token API fees. Self-hosted infrastructure costs dominate either way.
Llama 4 Community License has been in market a year with established legal interpretation. NVIDIA Open Model License is newer.
Choose Nemotron 3 Nano Omni when:
- ▸Multimodal agents that need vision, audio, and video in one pipeline
- ▸Document and PDF intelligence at scale
- ▸Voice-first agents and ASR-heavy workloads
- ▸Computer-use and GUI automation
- ▸Self-host on consumer hardware (NVFP4 runs on 24GB GPUs)
Choose Llama 4 Scout when:
- ▸Extreme long-context retrieval and code analysis (10M tokens)
- ▸Stable open-source ecosystem with mature tooling
- ▸Workloads that are text plus vision only and do not need audio or video
- ▸Teams standardized on the Llama family for fine-tuning continuity
Frequently Asked Questions
Which is better, Nemotron 3 Nano Omni or Llama 4 Scout?
It depends on your use case. Nemotron 3 Nano Omni from NVIDIA excels at multimodal agents that need vision, audio, and video in one pipeline, while Llama 4 Scout from Meta is better for extreme long-context retrieval and code analysis (10m tokens). See the full comparison above for detailed benchmarks and pricing.
How much does Nemotron 3 Nano Omni cost compared to Llama 4 Scout?
Nemotron 3 Nano Omni costs Free input and Free output per 1M tokens. Llama 4 Scout costs Free input and Free output per 1M tokens.
What is the context window difference between Nemotron 3 Nano Omni and Llama 4 Scout?
Nemotron 3 Nano Omni supports 256K tokens, while Llama 4 Scout supports 10M tokens.