Nemotron Nano 12B 2 VL
Description
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
At a Glance
Key pricing and model details available for this model.
Input price
$0.02
per 1M tokens
Output price
$0.06
per 1M tokens
Context window
128K
tokens
Hallucination rate
2.5%
Token Pricing
Token pricing normalized to per-million-token rates.
Input / 1M tokens
$0.02
Output / 1M tokens
$0.06
Cache Read / 1M tokens
Free
Token Pricing Details
Rates are shown per 1M tokens for easier comparison.
| Input / 1M tokens | $0.02 |
| Input unit | 1M tokens |
| Output / 1M tokens | $0.06 |
| Output unit | 1M tokens |
| Cache Read / 1M tokens | Free |
| Cache Read unit | 1M tokens |
Feature Availability
Capabilities explicitly listed in the current payload.
LLM
Available
Vision
Available
Function calling
Available
Reasoning
Available
Supported Parameters
Artificial Analysis
Index scores currently reported for this model.
Intelligence Index
10.1
Coding Index
5.9
Math Index
26.7
Category Radar
Aggregated from the benchmark values present for reasoning, code, math, and accuracy.
Benchmark Breakdown
Detailed benchmark results drawn from the current payload.
Intelligence Index
Overall 'how smart' score for an AI, combining reasoning, math, coding, and knowledge.
10.1
Reported score
Coding Index
How well the model handles real programming tasks.
5.9
Reported score
Math Index
Composite score measuring mathematical reasoning and problem-solving.
26.7
Reported score
MMLU-Pro
A broad and difficult knowledge-and-reasoning benchmark across many subjects.
64.9%
Reported score
GPQA
Graduate-level science questions designed to be difficult to shortcut.
43.9%
Reported score
HLE
A very hard expert-level exam across a wide range of subjects.
4.5%
Reported score
LiveCodeBench
Fresh programming tasks meant to test current coding ability.
34.5%
Reported score
SciCode
Coding tasks drawn from real scientific workflows.
17.6%
Reported score
AIME 2025
The 2025 AIME benchmark used to reduce data leakage concerns.
26.7%
Reported score
IFBench
Measures how precisely the model follows detailed instructions.
25.9%
Reported score
LCR
Tests long-context reasoning over large documents and conversations.
17%
Reported score
TerminalBench Hard
A harder coding-agent benchmark for complex multi-step terminal tasks.
0%
Reported score
Tau2
Evaluates realistic agent behavior in tool-using support workflows.
19.3%
Reported score
Code Samples
Quick start with the Routeway API
import OpenAI from 'openai';
const openai = new OpenAI({
baseURL: "https://api.routeway.ai/v1",
apiKey: "<YOUR_API_KEY>",
});
async function main() {
const completion = await openai.chat.completions.create({
model: "nemotron-nano-12b-v2-vl",
messages: [
{
role: "user",
content: "Explain quantum computing in simple terms"
}
]
});
console.log(completion.choices[0].message);
}
main();