We use essential cookies to make our site work. With your consent, we may also use non-essential cookies to improve user experience and analyze website traffic…

Nemotron 3 Nano Omni — the first multimodal model in the Nemotron 3 family, now on DeepInfra!

Nemotron Model Family

The Nemotron family spans Omni, Nano, Super, and specialized instruct variants, enabling you to

Get an API Key

Check Token Pricing

balance accuracy, reasoning depth, latency, and cost for your specific workload.

Omni for multimodal reasoning across text, audio, and video

Nano for maximum efficiency and stable inference

Super for multi-agent systems and advanced reasoning

Instruct variants for instruction-following and conversational workloads

Featured Model: nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning

Nemotron 3 Nano Omni is an open multimodal model built on a hybrid Mixture-of-Experts (MoE) architecture, engineered for high efficiency and strong accuracy across image, video, audio, and text inputs. It powers always-on sub-agents for computer use, document intelligence, and audio-video understanding—replacing fragmented vision, speech, and language pipelines with a single unified inference pass.

Price per 1M input tokens

$0.20

Price per 1M output tokens

$0.80

Release Date

04/28/2026

Context Size

262,144

Quantization

bfloat16

# Assume openai>=1.0.0
from openai import OpenAI

# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
    api_key="$DEEPINFRA_TOKEN",
    base_url="https://api.deepinfra.com/v1/openai",
)

chat_completion = openai.chat.completions.create(
    model="nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning",
    messages=[{"role": "user", "content": "Hello"}],
)

print(chat_completion.choices[0].message.content)
print(chat_completion.usage.prompt_tokens, chat_completion.usage.completion_tokens)

# Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
# 11 25
copy

Featured Model: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B

NVIDIA Nemotron 3 Super is a hybrid Mixture-of-Experts (MoE) model engineered for highest compute efficiency and accuracy in multi-agent applications and specialized agentic systems. It is optimized to run many collaborating agents per application on a single GPU, delivering high accuracy for reasoning, tool use, and instruction following.

Price per 1M input tokens

$0.10

Price per 1M output tokens

$0.50

Release Date

03/10/2026

Context Size

262,144

Quantization

bfloat16

# Assume openai>=1.0.0
from openai import OpenAI

# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
    api_key="$DEEPINFRA_TOKEN",
    base_url="https://api.deepinfra.com/v1/openai",
)

chat_completion = openai.chat.completions.create(
    model="nvidia/NVIDIA-Nemotron-3-Super-120B-A12B",
    messages=[{"role": "user", "content": "Hello"}],
)

print(chat_completion.choices[0].message.content)
print(chat_completion.usage.prompt_tokens, chat_completion.usage.completion_tokens)

# Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
# 11 25
copy

Featured Model: nvidia/Nemotron-3-Nano-30B-A3B

NVIDIA Nemotron 3 Nano is an open small reasoning model optimized for fast, cost-efficient inference in agentic and production workloads. Built with a hybrid Mixture-of-Experts (MoE) and Mamba-Transformer architecture, it delivers strong multi-step reasoning, high token throughput, stable latency with predictable cost, and efficient deployment for agent-based systems. Designed for real-world AI systems where reasoning can generate significantly more tokens per prompt, Nemotron Nano reduces compute cost while maintaining strong reasoning quality.

Price per 1M input tokens

$0.05

Price per 1M output tokens

$0.20

Release Date

12/15/2025

Context Size

262,144

Quantization

fp4

# Assume openai>=1.0.0
from openai import OpenAI

# Create an OpenAI client with your deepinfra token and endpoint
openai = OpenAI(
    api_key="$DEEPINFRA_TOKEN",
    base_url="https://api.deepinfra.com/v1/openai",
)

chat_completion = openai.chat.completions.create(
    model="nvidia/Nemotron-3-Nano-30B-A3B",
    messages=[{"role": "user", "content": "Hello"}],
)

print(chat_completion.choices[0].message.content)
print(chat_completion.usage.prompt_tokens, chat_completion.usage.completion_tokens)

# Hello! It's nice to meet you. Is there something I can help you with, or would you like to chat?
# 11 25
copy

Available Nemotron Models

The Nemotron family spans Nano, Super, and specialized instruct variants, enabling you to balance accuracy, reasoning depth, latency, and cost for your specific workload.

Nano for maximum efficiency and stable inference
Super for multi-agent systems and advance reasoning
Instruct variants for instruction-following and conversational workloads

Model	Context	$ per 1M input tokens	$ per 1M output tokens	Actions
NVIDIA-Nemotron-3-Super-120B-A12B	256k	$0.10	$0.50	View more
Nemotron-3-Nano-30B-A3B	256k	$0.05	$0.20	View more
Llama-3.3-Nemotron-Super-49B-v1.5	128k	$0.10	$0.40	View more
NVIDIA-Nemotron-Nano-9B-v2	128k	$0.04	$0.16	View more

FAQ

How do I integrate Nemotron models into my application?

You can integrate Nemotron models seamlessly using DeepInfra’s OpenAI-compatible API. Just replace your existing base URL with DeepInfra’s endpoint and use your DeepInfra API key—no infrastructure setup required. DeepInfra also supports integration through libraries like openai, litellm, and other SDKs, making it easy to switch or scale your workloads instantly.