Nemotron 3.5 Content Safety API: toman pricing and code
nvidia/nemotron-3.5-content-safety
You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Nemotron 3.5 Content Safety example: reading an image into JSON
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
# tip: a data URI works too — base64 the file and prefix it with data:image/jpeg;base64,
curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "nvidia/nemotron-3.5-content-safety",
"messages": [{
"role": "user",
"content": [
{"type": "text", "text": "Return only the invoice number and the total, as JSON."},
{"type": "image_url", "image_url": {"url": "https://example.com/factor.jpg"}}
]
}]
}'import base64, json
from openai import OpenAI
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
img = base64.b64encode(open("factor.jpg", "rb").read()).decode()
resp = client.chat.completions.create(
model="nvidia/nemotron-3.5-content-safety",
messages=[{
"role": "user",
"content": [
{"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
],
}],
response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))import OpenAI from "openai";
import { readFileSync } from "node:fs";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");
const resp = await client.chat.completions.create({
model: "nvidia/nemotron-3.5-content-safety",
messages: [{
role: "user",
content: [
{ type: "text", text: "Return the invoice number, the date and the total as JSON." },
{ type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
],
}],
response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));What is Nemotron 3.5 Content Safety good for?
Nemotron 3.5 Content Safety is one of NVIDIA's models. Put "nvidia/
What it can do beyond plain text: it reads images directly, which makes it a real option for invoices, forms and screenshots; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.
Pricing is 69,244 toman per 1M input tokens and 69,244 per 1M output tokens. A 1,000-word round trip on Nemotron 3.5 Content Safety lands near 180 toman. You are always charged for the usage the request actually reported, never for the estimate, and a failed request costs nothing.
The closest alternative with the same capabilities from a different provider is UI-TARS 7B: Nemotron 3.5 Content Safety works out about 30% more expensive, and its context window is larger. Both run on the same key and the same code, so trying the other one is a single string change.
To make the figure concrete: 100,000 toman of credit buys roughly 556 thousand-word requests on Nemotron 3.5 Content Safety, and every 1,000 toman is about 5,556 words of round trip. A job with one million input tokens and one million output tokens comes to 138,488 toman in total. Filling this model's 131,072-token window costs 9,076 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "nvidia/
NVIDIA has 12 models in our catalogue; the cheapest is Nemotron 3.5 Lightning at 122 toman per thousand words and the dearest Nemotron 3 Ultra at 1,350. Among the less common parameters it accepts logit_bias, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. It does not support response_format, tool_choice, tools, structured_outputs, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is Aion-2.0 at 131,072 tokens.
Good fits: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, pulling text and fields out of images.
NVIDIA Nemotron 3.5 Content Safety is a compact 4B-parameter multimodal guardrail model from NVIDIA, fine-tuned from Google Gemma-3-4B. It moderates both inputs to and responses from LLMs and VLMs, accepting...
Three real jobs, priced on this model
Each figure is derived from the prices above and moves when they do.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 132 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 319 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 9,694 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the standard variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| free | nvidia/nemotron-3.5-content-safety:free | 0 | 128,000 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
Frequently asked
- How do I call Nemotron 3.5 Content Safety from Iran?
- Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https:/
/ api.uttapen.ir/ v1 and set model to "nvidia/ nemotron-3.5-content-safety". Nothing else in your code changes. - What does Nemotron 3.5 Content Safety cost in toman?
- 69,244 toman per 1M input tokens and 69,244 toman per 1M output tokens; a 1,000-word request is around 180 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Nemotron 3.5 Content Safety take?
- Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 117,964 tokens.
- Does Nemotron 3.5 Content Safety support streaming and image input?
- Streaming (stream=true) works on every model here. Image input is accepted through image_url, as a data URI or a public URL.