nemotron-nano-12b-v2-vl:freeNVIDIA Nemotron Nano 2 VL is a 12B open multimodal reasoning model built for video understanding and document intelligence. Using a hybrid Transformer-Mamba design, it delivers high accuracy with lower latency and higher throughput. It handles text and multi-image inputs, excels at OCR, chart and document reasoning, and achieves leading benchmark results across major multimodal tests — including strong performance on long videos via Efficient Video Sampling. Open weights, data, and recipes are available under NVIDIA's permissive license, with broad deployment support across NeMo, NIM, and common runtimes.
Select an endpoint and copy a working example for this model.
from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="nemotron-nano-12b-v2-vl:free", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="nemotron-nano-12b-v2-vl:free",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
Decision guidance
See how this model compares to others from the same provider.
NVIDIA Nemotron 3.5 Lightning is an open Mixture-of-Experts (MoE) model with 30B total parameters and 3B active per token, optimized for high-throughput agentic workloads and efficient inference. Its lightweight active compute and open design make it well suited for specialized agents, domain-specific customization, and scalable production deployments where speed, cost efficiency, and adaptability are key.
NVIDIA Nemotron 3.5 Lightning is an open Mixture-of-Experts (MoE) model with 30B total parameters and 3B active per token, optimized for high-throughput agentic workloads and efficient inference. Its lightweight active compute and open design make it well suited for specialized agents, domain-specific customization, and scalable production deployments where speed, cost efficiency, and adaptability are key.
NVIDIA Nemotron 3 Nano Omni is an open 30B-A3B multimodal model designed as a perception and context sub-agent for enterprise agent systems. It supports text, image, video, and audio inputs with text output, enabling unified multimodal reasoning within a single inference loop. Built on a hybrid MoE Transformer–Mamba architecture with Conv3D video layers and Efficient Video Sampling (EVS), it delivers significantly improved efficiency for video reasoning—achieving ~2× higher throughput and 2.5× lower compute compared to separate pipelines. With up to 300K context length and extended thinking support, it is well suited for scalable, multimodal agent workflows.
Llama-3.3-Nemotron-Super-49B-v1.5 is a 49B reasoning and chat model derived from Llama-3.3-70B-Instruct, tuned for agent workflows like RAG and tool calling with a 128K context window. It combines supervised training with multiple RL stages to improve alignment, step-by-step reasoning, and tool use, while a NAS “Puzzle” architecture reduces memory and boosts throughput so it can run on a single H100/H200. It delivers strong results across math and coding benchmarks, supports toggleable reasoning modes, and is designed for efficient, reliable agent systems and long-context retrieval where accuracy and cost balance matter.