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Nemotron 3 Super API: toman pricing and code

nvidia/nemotron-3-super-120b-a12b

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Input · per 1M tokens
24,661 toman
Output · per 1M tokens
116,050 toman
One 1,000-word request ≈
183 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

Nemotron 3 Super example: JSON output against a schema

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

from openai import OpenAI
import json

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

schema = {
    "name": "ticket",
    "schema": {
        "type": "object",
        "properties": {
            "category": {"type": "string", "enum": ["fani", "mali", "forush"]},
            "priority": {"type": "integer", "minimum": 1, "maximum": 5},
            "summary": {"type": "string"},
        },
        "required": ["category", "priority", "summary"],
        "additionalProperties": False,
    },
    "strict": True,
}

resp = client.chat.completions.create(
    model="nvidia/nemotron-3-super-120b-a12b",
    messages=[{"role": "user", "content": "Ticket: "For two days I cannot download my invoice and I was charged twice.""}],
    response_format={"type": "json_schema", "json_schema": schema},
)
print(json.loads(resp.choices[0].message.content))

What is Nemotron 3 Super good for?

Nemotron 3 Super comes from NVIDIA; in uttapen you reach it with the model id "nvidia/nemotron-3-super-120b-a12b". It accepts up to 1,000,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 16,384 tokens. On price it sits in the "very cheap" band — cheaper than 71 and dearer than 335 of the other paid models in the catalogue.

What it can do beyond plain text: it supports tool calling, so it can invoke your own functions with valid arguments; 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 24,661 toman per 1M input tokens and 116,050 per 1M output tokens. A 1,000-word round trip on Nemotron 3 Super lands near 183 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 DeepSeek V3.2: Nemotron 3 Super works out roughly 1.4× cheaper, 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 546 thousand-word requests on Nemotron 3 Super, and every 1,000 toman is about 5,464 words of round trip. A job with one million input tokens and one million output tokens comes to 140,711 toman in total. Filling this model's 1,000,000-token window costs 24,661 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "nvidia/nemotron-3-super-120b-a12b: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: 1,000,000 against 262,144 tokens. Maximum answer length differs as well: 16,384 against 235,929 tokens. On parameters, this one takes frequency_penalty, logit_bias, logprobs, min_p.

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, logprobs, min_p, reasoning_effort, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Nova 2 Lite at 1,000,000 tokens.

Good fits: high-volume work such as classification, tagging and bulk summarising, logic puzzles and code review, agents that reach out to APIs and databases, extracting data against a fixed schema, analysing a long document or codebase in one request.

Provider's own description

NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid MoE model, activating just 12B parameters for maximum compute efficiency and accuracy in complex multi-agent applications. Built on a hybrid Mamba-Transformer...

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 out83 toman
Summarising a ten-page document4,000 in + 600 out168 toman
Classifying a thousand short rows120,000 in + 20,000 out5,280 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-super-120b-a12b:free0262,144

Put the variant's id verbatim in the model field; nothing else in your code changes.

Frequently asked

How do I call Nemotron 3 Super 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-super-120b-a12b". Nothing else in your code changes.
What does Nemotron 3 Super cost in toman?
24,661 toman per 1M input tokens and 116,050 toman per 1M output tokens; a 1,000-word request is around 183 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Nemotron 3 Super take?
Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 16,384 tokens.
Does Nemotron 3 Super support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.