Nvidia: Nemotron 3.5 Content Safety
Safety model for policy screening, moderation, and risk-aware routing workflows
- Model lab
- Nvidia
- Model family
- nemotron
Quickstart
Use your Moduck API Key, set the API base URL to https://api.moduck.ai/v1, and set the model parameter to moduck/nvidia/nemotron-3-5-content-safety.
import os
import requests
base_url = "https://api.moduck.ai/v1"
api_key = os.environ["MODUCK_API_KEY"]
model_id = "moduck/nvidia/nemotron-3-5-content-safety"
response = requests.post(
f"{base_url}/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
json={
"model": model_id,
"messages": [{"role": "user", "content": "Hello!"}],
"stream": False,
},
timeout=60,
)
response.raise_for_status()
print(response.json())Use the familiar OpenAI-style chat request format to make model integration easier.
Nemotron 3.5 Content Safety API Pricing
| Type | Price information |
|---|---|
| Input | USD 0.04 / 1M tokens |
| Output | USD 0.12 / 1M tokens |
Performance
Measured on the Moduck gateway across live traffic, hour by hour over the last 24 hours. Time to first token is how long before anything appears; throughput is how fast the answer is written after that; end-to-end latency is the whole round trip. Figures are medians unless the card says otherwise.
Performance trends
Availability
Uptime is the share of time at least one provider serving this model responded. Availability is stricter: the share of requests actually served. A provider that answers but fails part of the traffic lowers availability without lowering uptime. Both cover the last three days.
Nemotron 3.5 Content Safety Compare models
| Model | Input price | Output price | Context window | Capabilities |
|---|---|---|---|---|
| Nemotron 3.5 Content SafetyThis page | USD 0.04 / 1M tokens | USD 0.12 / 1M tokens | 128,000 | Text output · Reasoning |
| Llama Nemotron Rerank VL 1B v2 | USD 0.008 / 1M tokens · Maximum input 10240 tokens · Service tier default | Not billed on output | 128,000 | Text output |
| Llama Nemotron Embed VL 1B v2 | USD 0.008 / 1M tokens · Maximum input 10240 tokens · Service tier default | Not billed on output | 32,768 | Text output |
| Nemotron 3.5 Super VL 120B A12B | USD 0.24 / 1M tokens | USD 0.72 / 1M tokens | 1,048,576 | Text output · Reasoning · Tool calling |
| Nemotron 3.5 Lightning 30B A3B | USD 0.04 / 1M tokens | USD 0.16 / 1M tokens | 262,144 | Text output · Reasoning · Tool calling |
Model specifications
- Input
- text, image
- Output
- text
- Context window
- 128,000
- Maximum output
- 8,192
- Release date
- 2026-06-04
- Attachments
- Yes
- Reasoning
- Yes
- Tool calling
- No
- Temperature setting
- Yes
- Open weights
- Yes
Frequently asked questions
What is Nemotron 3.5 Content Safety for?
Safety model for policy screening, moderation, and risk-aware routing workflows
What inputs and outputs does Nemotron 3.5 Content Safety support?
Recorded inputs: text, images; outputs: text.
How is Nemotron 3.5 Content Safety priced?
The pricing table shows the current recorded Moduck price. For example: Input USD 0.04 / 1M tokens; Output USD 0.12 / 1M tokens. See the pricing table for all units, variants, and conditions.
What specifications are recorded for Nemotron 3.5 Content Safety?
Current model records list Context window 128,000 tokens; Maximum output 8,192 tokens.
What model parameter does the Quickstart use?
The example uses moduck/nvidia/nemotron-3-5-content-safety. This is a cataloged Moduck public or reserved ID; check your account’s available models before calling it.
Can I call this model through the Moduck API now?
This page has no verified Moduck API call for this model. A Quickstart example or reserved ID does not establish access; check the models available to your account.