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Nemotron 3.5 Content Safety API: toman pricing and code

nvidia/nemotron-3.5-content-safety

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Input · per 1M tokens
58,025 toman
Output · per 1M tokens
58,025 toman
One 1,000-word request ≈
151 toman

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"}}
      ]
    }]
  }'

What is Nemotron 3.5 Content Safety good for?

Nemotron 3.5 Content Safety is one of NVIDIA's models. Put "nvidia/nemotron-3.5-content-safety" in the model field and the rest of your code stays as it is. Nemotron 3.5 Content Safety keeps 131,072 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 117,964 tokens. On price it sits in the "very cheap" band — cheaper than 33 and dearer than 373 of the other paid models in the catalogue.

What it can do beyond plain text: it reads images directly, which makes it a real option for invoices, forms and screenshots; it returns schema-valid JSON through response_format, ready to hand to your code; 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 58,025 toman per 1M input tokens and 58,025 per 1M output tokens. A 1,000-word round trip on Nemotron 3.5 Content Safety lands near 151 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 roughly 1.3× 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 662 thousand-word requests on Nemotron 3.5 Content Safety, and every 1,000 toman is about 6,623 words of round trip. A job with one million input tokens and one million output tokens comes to 116,050 toman in total. Filling this model's 131,072-token window costs 7,605 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "nvidia/nemotron-3.5-content-safety:free", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "free". The context windows differ too: 131,072 against 128,000 tokens. Maximum answer length differs as well: 117,964 against 8,192 tokens. On parameters, this one takes frequency_penalty, logit_bias, min_p, presence_penalty.

NVIDIA has 10 models in our catalogue; the cheapest is Nemotron 3 Nano 30B A3B at 94 toman per thousand words and the dearest Nemotron 3 Ultra at 1,414. Among the less common parameters it accepts logit_bias, min_p, repetition_penalty, top_k — all through the standard request body. It does not support 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, extracting data against a fixed schema.

Provider's own description

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.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out110 toman
Summarising a ten-page document4,000 in + 600 out267 toman
Classifying a thousand short rows120,000 in + 20,000 out8,124 toman

Other variants of this model

Same core model, different execution terms and different price. This page is the standard variant.

VariantModel idOutput / 1MContext
freenvidia/nemotron-3.5-content-safety:free0128,000

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?
58,025 toman per 1M input tokens and 58,025 toman per 1M output tokens; a 1,000-word request is around 151 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. Structured output through response_format works too.