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NVIDIA Nemotron 3 Nano 4B vs NVIDIA Nemotron 3 Nano Omni 30B-A3B Reasoning
Put two to four models side by side on the fields that decide a deployment: context, price, region, license, languages, hosting, and what is verified.
| NVIDIA Nemotron 3 Nano 4B | NVIDIA Nemotron 3 Nano Omni 30B-A3B Reasoning | |
|---|---|---|
| Provider | NVIDIA | NVIDIA |
| Parameters | 3.97B | 33B |
| Context window | 262k | Not verified: the provider has not documented this claim. |
| License | other | other |
| Modalities | text | text, vision, audio |
| Release date | 07 Mar 2026 | 20 Apr 2026 |
| Languages | Strong: en Adequate: — | Not verified: the provider has not documented this claim. |
| Open weights | yes | yes |
| Tasks | Low cost, Reasoning, Retrieval (RAG), Summarisation | Reasoning, Vision, Multilingual support |
Where two models differ, this table shows the difference and stops. LLM EU publishes no winner badge without a documented score for that task, and a missing figure stays missing instead of becoming a zero.
When to use it
NVIDIA Nemotron 3 Nano 4B
Strengths
- long context for a 4B model
- high published maths scores for its size
- runs on a single small accelerator
Limits
- English only, per the model card
- vendor licence rather than an OSI one
- benchmark figures come from NVIDIA’s own harness
Tasks
NVIDIA Nemotron 3 Nano Omni 30B-A3B Reasoning
Strengths
- open weights are published on Hugging Face and can be downloaded without a vendor agreement
- instruction-tuned checkpoint with a chat template in the repository
- speech recognition runs locally, so audio never leaves the operator premises
Limits
- catalogue only — LLM EU does not run this model, and nothing on this card implies an LLM EU endpoint
- no evaluation has been run by LLM EU
- the nvidia-open-model-agreement (other) licence is a custom agreement whose commercial terms were not established from its published text, so commercial serving is not permitted until a human clears it; the router must not route to it
- the published configuration records no context window, so the card carries none
- at 33B parameters, self-hosting needs a high-memory accelerator
- vision checkpoints are heavier to serve than their parameter count alone suggests, because the image encoder and pre-processing run alongside the language model
Tasks
Tasks: Reasoning
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