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Nemotron Nano 12B 2 VL (free)

Nemotron Nano 12B 2 VL (free) is NVIDIA’s open 12B-parameter multimodal vision-language model, offered as a no-cost endpoint on OpenRouter and similar platforms.

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What is Nemotron Nano 12B 2 VL (free)?

Nemotron Nano 12B 2 VL (free) is a hosted, no-cost variant of NVIDIA’s Nemotron Nano v2 12B vision-language model for multimodal reasoning across text and visual inputs. It is mainly used for document intelligence tasks such as reading and extracting information from documents, screens, and tables, as well as visual question answering and image-text analysis. It also targets video frames and multi-image understanding for summarization, captioning, and retrieval-augmented generation workflows. This model belongs to NVIDIA’s Nemotron Nano 2 family of hybrid Mamba–Transformer models, derived from the Nemotron-Nano-12B-v2 base and extended into the V2 VL vision-language line.

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

Provider Input Output Cache read /M Latency Throughput Uptime
NVIDIA NIM ~$0.30 ~$0.30 — ~140ms ~70 tps ~99.9%
RunPod ~$0.22 ~$0.22 — ~180ms ~55 tps ~99.5%
Lambda Cloud ~$0.28 ~$0.28 — ~190ms ~50 tps ~99.9%
Replicate ~$0.35 ~$0.35 — ~210ms ~40 tps ~99.0%

Try this model

Test Nemotron Nano 12B 2 VL (free) right here — free to start.

Nemotron Nano 12B 2 VL (free)
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Code snippet

Call the model through the OpenAI-compatible API.

python 
                                        from openai import OpenAI
                                            
                                            client = OpenAI(
                                                api_key="YOUR_API_KEY",
                                                base_url="https://inference.example.com/v1"
                                            )
                                            
                                            response = client.chat.completions.create(
                                                model="nvidia/nemotron-nano-12b-2-vl-free",
                                                messages=[
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ],
                                            )
                                            
                                            print(response.to_json())
                                        
                                    
                                        {
                                                "model": "nvidia/nemotron-nano-12b-2-vl-free",
                                                "messages": [
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ]
                                            }

5 Core Capabilities

  • Vision-Language Reasoning

    Understands images alongside text, allowing visual question answering, captioning, and grounded reasoning over visual scenes and objects.

  • Conversational Assistance

    Engages in multi-turn dialogue, following instructions, answering questions, and maintaining context for helpful, coherent conversations.

  • Screen and UI Reasoning

    Interprets screenshots or interface-like visuals, identifying elements to support automated agents and UI understanding tasks.

  • Optical Character Recognition

    Reads and extracts textual content from images, enabling understanding of documents, signs, and screenshots containing embedded text.

  • Multilingual Understanding

    Understands and generates multiple languages, enabling cross-lingual question answering, summarization, and basic translation between supported languages.

6 Most Valuable Use Cases

  • Document OCR & Parsing
  • Contract & Policy Review
  • Video Content Analysis
  • Customer Support Assistant
  • Tool-Using AI Agents
  • Case Monitoring Dashboards

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Unified AI Routing

    Dynamically route each request across models and providers based on cost, latency, or quality—no code changes, just smarter traffic from a single endpoint.

    One endpoint, every model

  • Cost-Aware Control

    Enforce per-project budgets, choose cheaper equivalents automatically, and get transparent spend analytics so you can scale AI usage without surprise invoices.

    Optimize spend by default

  • Resilient Fallbacks

    Define provider-agnostic fallback chains so requests transparently fail over to backup models, keeping your production apps online even during provider outages.

    Stay online, automatically

  • Full-Stack Observability

    Trace every request across models, providers, and tenants with metrics, logs, and structured events to debug faster and ship safer in production.

    See every token hop

  • Task-Level Abstractions

    Call AI by intent—chat, embed, classify, extract—while LLM.API selects and tunes the right model, simplifying integration and future-proofing your stack.

    Code to tasks, not models

  • High-Throughput Batch

    Process millions of inferences via optimized batch pipelines with built-in retries, rate control, and cost tracking, without hand-rolling job infrastructure.

    Scale to millions of calls

When to Use — When NOT to Use

Use it if...

  • You need a free, vision-language model for basic image and text understanding.
  • You need to prototype multimodal features without paying for GPU-hosted proprietary APIs.
  • Your use case involves simple visual question answering on small, non-sensitive images.
  • Your use case involves lightweight on-device experimentation with open NVIDIA vision-language models.
  • You need a compact VL model for educational demos or internal tooling experiments.
  • Your use case involves extracting simple captions or tags from product or UI screenshots.

Avoid if...

  • You need state-of-the-art vision-language performance on complex, high-stakes production workloads.
  • Your workload requires very long-context multimodal reasoning over many images and documents.
  • You need advanced code generation, tool use, or agentic reasoning beyond basic capabilities.
  • Your workload requires highly optimized inference latency and throughput at massive enterprise scale.
  • You need strong robustness, safety tuning, and reliability guarantees for regulated industry use.
  • Your workload requires best-in-class pure text reasoning rather than primarily vision-language tasks.

Frequently Asked Questions

  • What is Nemotron Nano 12B 2 VL (free)?

    Nemotron Nano 12B 2 VL (free) is an NVIDIA 12B-parameter vision-language model focused on efficient multimodal understanding and generation.

  • What is Nemotron Nano 12B 2 VL (free) best suited for?

    It is best for lightweight multimodal tasks like image captioning, visual question answering, and simple document understanding where low cost matters.

  • How much does it cost to use Nemotron Nano 12B 2 VL (free) via LLM.API?

    This tier is offered as a free model on LLM.API, so you are not billed per-token for its usage.

  • What is the context window of Nemotron Nano 12B 2 VL (free)?

    Nemotron Nano 12B 2 VL (free) supports a context window of up to 8,192 tokens for text input and conversation history.

  • What modalities does Nemotron Nano 12B 2 VL (free) support?

    It supports both text and image inputs and produces text-only outputs, enabling typical vision-language workflows.

  • How fast is Nemotron Nano 12B 2 VL (free) on LLM.API?

    As a 12B-parameter model, it generally offers lower latency and faster responses than larger multimodal models on comparable hardware.

  • How do I call Nemotron Nano 12B 12B 2 VL (free) through LLM.API?

    You select it by its exact model name in the LLM.API request, keeping the same unified chat or completion API schema.

  • How does Nemotron Nano 12B 2 VL (free) compare to larger vision-language models?

    It trades some reasoning depth and fine-grained visual understanding for significantly lower compute cost and faster inference.

  • What are the main limitations of Nemotron Nano 12B 2 VL (free)?

    It may struggle with complex reasoning, high-resolution dense visual details, very long contexts, and domain-specialized tasks compared to larger models.

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