See how ChatGPT, Perplexity and Google AI Overviews describe you today
Free AI Visibility AuditNemotron 3 Nano Omni is NVIDIA's open multimodal model, a 30B-A3B system built to act as a perception and context sub-agent inside larger enterprise agent stacks, and it is offered free to run.
01 / decision
Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 232 models we track, not against an absolute standard.
Capability
Not measured
No published benchmark scores yet
Input / 1M tokens
Free
field median $0.41
Output / 1M tokens
Free
field median $2.00
Context
256K
field median 500K
02 / overview
Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.
The job it was built for, and the job it is not for.
Nemotron 3 Nano Omni is a small open multimodal model that reads text, images, video and audio and turns them into something the rest of an agent system can act on. NVIDIA positions it as a sub-agent, the component that handles perception and context rather than the one that owns the reasoning or the final answer. It is not intended to be the single model behind a general assistant product.
Where it sits in its line, and when a sibling is the better pick.
Nemotron 3 Nano Omni appeared in April 2026 as the Nano tier of NVIDIA's Nemotron 3 family, the small end rather than the flagship. It is the right pick when you need many cheap perception calls across mixed media inside a pipeline that has a stronger model above it. It is the wrong pick when you want one model to do the reasoning, the writing and the final customer-facing response on its own.
The API, the apps it powers, and what its limits let you do.
Nemotron 3 Nano Omni is reached through the API as an open model, which means it can also be pulled and self-hosted rather than only rented. The wide context window lets you hand it long transcripts, document sets or extended video and audio alongside text in a single call, and the large output ceiling means it can return full structured extractions rather than short summaries. In practice it sits inside agent frameworks, feeding parsed context to whatever model does the deciding.
What changes because this exists, or why it does not.
A four-modality model at this size, free to call and open to host, makes it reasonable to run perception on everything instead of triaging what is worth processing. Work that previously meant stitching together a separate speech model, a vision model and a document parser can collapse into one component. That is a plumbing change more than a capability leap, but it is the kind of change that shows up in monthly bills.
Follows Nemotron 3 Super. Superseded by Nemotron 3.5 Content Safety. See the whole line.
03 / evidence
Split by category, so a strong number never hides a thin evidence base. Verified means we read it on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.
No published benchmark scores for this model yet.
We publish a score only where we can link the result to where it was published. Until a benchmark result for this model exists in a source we read, this section stays empty rather than being filled with a provider's marketing figure.
How we decide what counts as evidence04 / ledger
Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.
Nothing in the ledger yet.
Each row here carries a score, the benchmark it came from, what the leading model scored on the same test, and a link to the published result. Rows appear as results are published and read.
How we decide what counts as evidence05 / capability
Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: ranking against models nobody tested would rank who published, not who is better.
No category scores to shape yet.
A category score is the weighted mean of the benchmarks published for it. With no published rows there is nothing to average, and an empty chart drawn at zero would say something false.
How we decide what counts as evidence06 / cost
List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.
| Charge | Price | Unit | Read on | Source |
|---|---|---|---|---|
| Input | Free | per 1M tokens | 2026-09-24 | Check |
| Output | Free | per 1M tokens | 2026-09-24 | Check |
Nemotron 3 Nano Omni is listed at no cost for both input and output, which puts it at the floor of the market rather than merely cheap. The real cost sits in the infrastructure if you self-host it, so compare it on GPU time and engineering effort, not on token rates.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | Free |
| A busy support assistant | 200M tokens | 40M tokens | Free |
| A document pipeline | 1000M tokens | 100M tokens | Free |
List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.
07 / specs
As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.
| Context window | 256,000 tokens |
|---|---|
| Maximum output | 65,536 tokens |
| Modalities | text, audio, image, video |
| Released | 28/04/2026 |
| Status | Current |
| Catalogue identifier | nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free |
08 / lineage
What this model replaced, what replaced it, and what else its provider has in the field.
Also from NVIDIA
09 / line
Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.
Came before
← Nemotron 3 SuperCame after
Nemotron 3.5 Content Safety →Ordered by release date and worked out from the naming, so a new member slots in as soon as its page exists. A retired model keeps its page and its place in the line.
10 / notes
What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.
What it changes for you
Models like this sit upstream of the answer, not at the point of citation, so its arrival does not change who gets named in an AI response. What it does change is the volume and variety of source material an agent can afford to ingest, including video, audio and screenshots of your product. Documentation, demos and recorded walkthroughs become machine-readable inputs in a way they were not when text was the only cheap modality.
Buyers rarely meet Nemotron 3 Nano Omni directly, there is no consumer chat app attached to it. They encounter it through the API, through NVIDIA's enterprise agent tooling, and through whatever internal product a vendor has built on top of it, where it does the seeing and hearing and something else does the talking.
Nothing published here yet. Changes appear within hours of a provider announcing them.
11 / questions
The things people ask about this model, answered from what is on this page rather than from anywhere else.
Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.