Nemotron Nano 9B V2 — NVIDIA | Modeldex

Nemotron Nano 9B V2

Active

NVIDIA-Nemotron-Nano-9B-v2 is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and...

Updated 4 days agoStructured data from Modeldex catalog

Budget

Knowledge cutoff

Mar 31, 2025(1.1 years ago)

API release

Sep 5, 2025(8 months ago)

Not enough benchmark coverage yet for an Intelligence Index — needs at least 3 results across 2 categories.

Overview

History

Nemotron Nano 9B V2 became available via the NVIDIA API on 2025-09-05.

Training & availability

Training data has a knowledge cutoff of 2025-03-31 — information about events after that date is unlikely to appear in the model's responses. NVIDIA has not released the underlying model weights — access is via their hosted API only.

Capabilities

Context window: 131K tokens.
Input modalities: text.

Recommended for: cheap.

Limitations

The knowledge cutoff is 12 months old — this model will not know about recent events, releases, or API changes.
Text-only — cannot process images, audio, or video inputs.

Pricing

Input: $0.0400 per 1M tokens
Output: $0.1600 per 1M tokens

Use the cost calculator above to estimate monthly spend for your workload.

Quick start

Minimal example using the OpenRouter API. Copy, paste, replace the key.

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="sk-or-...",
)
resp = client.chat.completions.create(
    model="nvidia/nemotron-nano-9b-v2",
    messages=[{"role": "user", "content": "Explain quantum computing in one sentence."}],
)
print(resp.choices[0].message.content)

Cost calculator

Estimate your monthly bill. Presets are typical workload sizes.

Input tokens / month5.0M

@ $0.04/1M

Output tokens / month2.0M

@ $0.16/1M

Input cost

$0.2

5.0M × $0.04/1M

Output cost

$0.32

2.0M × $0.16/1M

Total / month

$0.52

$6.24 / year

Providers & performance

1 provider

Multi-provider inference routes for this model — sorted by throughput. Latency is time-to-first-token; throughput is output tokens per second. Data from OpenRouter, measured over the last 30 minutes.

Provider	Throughput	Latency (TTFT)	Input $ / 1M	Output $ / 1M	Context	Quant	Supports
DeepInfra	115tok/s	166ms	$0.04	$0.16	131K	bf16	tools · json

Popularity

Signals from open-source communities — not a quality measure, but useful for gauging adoption among developers.

🤗

HuggingFace downloads

525.5K/ month

HuggingFace likes

488

Integrations & tooling support

Tool calling: Not supported
Structured outputs: Not supported

Price vs quality

Budget pricing

Priced low — good for high-volume tasks. Quality tier pending more benchmark coverage.