Nemotron 3.5 Super VL
nemotron-3.5-super-vlNVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.
- Context
- 1M tokens
- Endpoint
Service Status
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Pricing
Quick Start
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-3.5-super-vl", 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-3.5-super-vl",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )Supported Parameters
API docsmodelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyCursor IDE Model IDs
Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
Compare with Other Models
See how this model compares to others from the same provider.
Nemotron 3.5 Super VL (Free)
NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.
- Context
- 1M
- Input
- $0/M
- Output
- $0/M
Nemotron 3.5 ASR Streaming Multilingual 0.6B
NVIDIA Nemotron 3.5 ASR multilingual speech-to-text (0.6B). Billed per second of audio.
- Context
- N/A
- Input
- — (Not priced per input token)
- Output
- — (Not priced per output token)
Nemotron 3 Nano Omni (Free)
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.
- Context
- 256K
- Input
- $0/M
- Output
- $0/M
Llama 3.3 Nemotron Super 49B V1.5
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.
- Context
- 131.1K
- Input
- $0.05/M
- Output
- $0.20/M