Atlas / Models / Nvidia / NVIDIA: Nemotron Nano 12B 2 VL

NVIDIA: Nemotron Nano 12B 2 VL✓ Catalog verified

nvidia/nemotron-nano-12b-v2-vl

NVIDIA Nemotron Nano 2 VL is a 12-billion-parameter open multimodal reasoning model designed for video understanding and document intelligence. It introduces a hybrid Transformer-Mamba architecture, combining transformer-level accuracy with Mamba’s memory-efficient sequence modeling for significantly higher throughput and lower latency. The model supports inputs of text and multi-image documents, producing natural-language outputs. It is trained on high-quality NVIDIA-curated synthetic datasets optimized for optical-character recognition, chart reasoning, and multimodal comprehension. Nemotron Nano 2 VL achieves leading results on OCRBench v2 and scores ≈ 74 average across MMMU, MathVista, AI2D, OCRBench, OCR-Reasoning, ChartQA, DocVQA, and Video-MME—surpassing prior open VL baselines. With Efficient Video Sampling (EVS), it handles long-form videos while reducing inference cost. Open-weights, training data, and fine-tuning recipes are released under a permissive NVIDIA open license, with deployment supported across NeMo, NIM, and major inference runtimes.

Input price
$0.20 /1M
Output price
$0.60 /1M
Context
131K
Modalities
imagetextvideo
Released
Oct 28, 2025
Tool calling
Atlas signal
88/100
01

Overview

NVIDIA Nemotron Nano 2 VL is a 12-billion-parameter open multimodal reasoning model designed for video understanding and document intelligence. It introduces a hybrid Transformer-Mamba architecture, combining transformer-level accuracy with Mamba’s memory-efficient sequence modeling for significantly higher throughput and lower latency. The model supports inputs of text and multi-image documents, producing natural-language outputs. It is trained on high-quality NVIDIA-curated synthetic datasets optimized for optical-character recognition, chart reasoning, and multimodal comprehension. Nemotron Nano 2 VL achieves leading results on OCRBench v2 and scores ≈ 74 average across MMMU, MathVista, AI2D, OCRBench, OCR-Reasoning, ChartQA, DocVQA, and Video-MME—surpassing prior open VL baselines. With Efficient Video Sampling (EVS), it handles long-form videos while reducing inference cost. Open-weights, training data, and fine-tuning recipes are released under a permissive NVIDIA open license, with deployment supported across NeMo, NIM, and major inference runtimes.

Access: available through the official Nvidia API. Context window: 131K.

Pricing and metadata from the ModelsAtlas catalog.Last refreshed Aug 5, 2026
02

Specifications

API identifiernvidia/nemotron-nano-12b-v2-vl
ProviderNvidia
Model typeMultimodal LLM
Context window131K catalog
Input modalitiesimage · text · video
Output modalitiestext
ReleasedOct 28, 2025
ModeratedNo
Architecture modalitytext+image+video->text
Supported parameters
frequency_penaltyinclude_reasoningmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatseedstoptemperaturetop_ktop_p
Source: provider documentation + catalog feedMethodology →

API defaults

Default parameters
{
  "top_p": null,
  "temperature": null,
  "frequency_penalty": null
}
03

Pricing

Live pricing components for nvidia/nemotron-nano-12b-v2-vl as published in the catalog.

$0.20
Input /1M
$0.60
Output /1M
Per request
Per image
Pricing componentRaw unit priceNormalizedUnit
Prompt tokens$2e-7$0.20 / 1MPer input token
Completion tokens$6e-7$0.60 / 1MPer output token
Request feeN/AN/APer request
Image feeN/AN/APer image unit
Web search feeN/AN/APer search request
Source: catalog pricing feedCompare all pricing →Cheapest models →
04

Cost calculator

Estimate monthly spend from your own token volumes.

Scale / Volume

Estimated Total Cost

$0.22Calculated from current list pricing in the ModelsAtlas catalog.
05

Capabilities

CapabilityStatusWhat it means
Visual Understanding✓ SupportedImage and document analysis support
Audio ProcessingNot advertisedSpeech and voice aligned flows
Tool CallingNot advertisedSupports tools / function calling
Self-HostingNot advertisedDeploy outside managed APIs
Derived from catalog capability tags and supported parameters
06

Capability signals

Directional signals derived from model metadata and capability tags — not official benchmark submissions.

NVIDIA: Nemotron Nano 12B 2 VL
MMLU Signal (Reasoning)
97signal
Coding Signal (HumanEval proxy)
82signal
Math Signal (GSM8K proxy)
92signal
Science Signal (GPQA proxy)
79signal
Estimated from metadata — not an official benchmark runAll benchmarks →Methodology →
07

Quick start

Call NVIDIA: Nemotron Nano 12B 2 VL through an OpenAI-compatible client.

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="nvidia/nemotron-nano-12b-v2-vl",
    messages=[{"role": "user", "content": "Explain quantum physics."}]
)

print(response.choices[0].message.content)
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Alternatives

Closest models by context window from a different provider.

09

Sources & attribution

Pricing and metadata are maintained in the ModelsAtlas catalog.

Capability bars use metadata tags — directional estimates onlyHow we source and verify data →
10

Frequently asked questions

How much does NVIDIA: Nemotron Nano 12B 2 VL cost?

$0.20 per 1M input tokens and $0.60 per 1M output tokens on the official Nvidia API.

What is the context window of NVIDIA: Nemotron Nano 12B 2 VL?

NVIDIA: Nemotron Nano 12B 2 VL supports up to 131K tokens of context.

Does NVIDIA: Nemotron Nano 12B 2 VL support tool / function calling?

NVIDIA: Nemotron Nano 12B 2 VL does not advertise first-class tool/function calling. Check vendor docs for the latest capabilities.

How do I access NVIDIA: Nemotron Nano 12B 2 VL?

Use the official Nvidia API with the model id `nvidia/nemotron-nano-12b-v2-vl`. See the Quick start section above for code examples.