How do I call Nemotron 3 Ultra 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-ultra-550b-a55b". Nothing else in your code changes.

What does Nemotron 3 Ultra cost in toman?

207,732 toman per 1M input tokens and 830,926 toman per 1M output tokens; a 1,000-word request is around 1,350 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does Nemotron 3 Ultra take?

Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 182,520 tokens.

Does Nemotron 3 Ultra 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.

Nemotron 3 Ultra API: toman pricing and code

nvidia/nemotron-3-ultra-550b-a55b

Tool callingReasoningJSON output

Create a key Try it

Input · per 1M tokens

207,732 toman

Output · per 1M tokens

830,926 toman

One 1,000-word request ≈

1,350 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 41,546 toman per 1M tokens.Pricing and top-ups

Nemotron 3 Ultra example: tool calling

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

PythonNode.jscurl

from openai import OpenAI
import json

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

tools = [{
    "type": "function",
    "function": {
        "name": "check_stock",
        "description": "Returns the stock level of a product",
        "parameters": {
            "type": "object",
            "properties": {"sku": {"type": "string"}},
            "required": ["sku"],
        },
    },
}]

messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="nvidia/nemotron-3-ultra-550b-a55b", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]

# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}

messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="nvidia/nemotron-3-ultra-550b-a55b", messages=messages, tools=tools)
print(final.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });

const tools = [{
  type: "function",
  function: {
    name: "check_stock",
    description: "Returns the stock level of a product",
    parameters: { type: "object", properties: { sku: { type: "string" } }, required: ["sku"] },
  },
}];

const messages = [{ role: "user", content: "How many of the Nike NK-42 shoe are in stock?" }];
const first = await client.chat.completions.create({ model: "nvidia/nemotron-3-ultra-550b-a55b", messages, tools });
const call = first.choices[0].message.tool_calls[0];

const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };

messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "nvidia/nemotron-3-ultra-550b-a55b", messages, tools });
console.log(final.choices[0].message.content);

curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nvidia/nemotron-3-ultra-550b-a55b",
    "messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "check_stock",
        "parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
      }
    }]
  }'

What is Nemotron 3 Ultra good for?

Nemotron 3 Ultra is one of NVIDIA's models. Put "nvidia/nemotron-3-ultra-550b-a55b" in the model field and the rest of your code stays as it is. Nemotron 3 Ultra keeps 262,144 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 182,520 tokens. On price it sits in the "mid-range" band — cheaper than 175 and dearer than 220 of the other paid models in the catalogue.

Capabilities available on this id: 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.

For a back-of-envelope figure: about 1,350 toman per 1,000-word exchange (207,732 in, 830,926 out, per 1M tokens). It supports cached input: repeated context is billed at 41,546 toman per 1M, which matters a lot if your system prompt is long. 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 GPT Audio Mini, and Nemotron 3 Ultra costs about the same, 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 74 thousand-word requests on Nemotron 3 Ultra, and every 1,000 toman is about 741 words of round trip. A job with one million input tokens and one million output tokens comes to 1,038,658 toman in total. Filling this model's 262,144-token window costs 54,456 toman on the input side alone, which is the real reason to keep conversation history short.

This id gets confused with "nvidia/nemotron-3-ultra-550b-a55b: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: 262,144 against 1,000,000 tokens. Maximum answer length differs as well: 182,520 against 65,536 tokens. On parameters, this one takes frequency_penalty, logit_bias, min_p, presence_penalty.

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, reasoning_effort, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Aion 3.5 at 262,144 tokens.

What to use it for: product chatbots and internal assistants, where cost and quality have to balance, 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 Ultra is an open frontier-reasoning and orchestration model from NVIDIA, with 55B active parameters out of 550B total (MoE). Built on a hybrid Transformer-Mamba mixture-of-experts architecture, it...

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	644 toman
Summarising a ten-page document	4,000 in + 600 out	1,329 toman
Classifying a thousand short rows	120,000 in + 20,000 out	41,546 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

644 toman

Summarising a ten-page document

4,000 in + 600 out

1,329 toman

Classifying a thousand short rows

120,000 in + 20,000 out

41,546 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-ultra-550b-a55b:free	0	1,000,000

Variant

Model id

Output / 1M

Context

free

nvidia/nemotron-3-ultra-550b-a55b:free

1,000,000

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

Price history

Date	Input (toman/1M)	Output (toman/1M)
11 Oct 2026	207,732	830,926
3 Oct 2026	173,110	761,683
2 Oct 2026	207,732	830,926
2 Oct 2026	173,110	761,683
19 Sep 2026	207,732	830,926
17 Sep 2026	216,387	1,081,935
16 Sep 2026	207,732	830,926
16 Sep 2026	216,387	1,081,935
16 Sep 2026	207,732	830,926
16 Sep 2026	216,387	1,081,935
14 Sep 2026	207,732	830,926
7 Sep 2026	216,387	1,081,935

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call Nemotron 3 Ultra 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-ultra-550b-a55b". Nothing else in your code changes.
What does Nemotron 3 Ultra cost in toman?: 207,732 toman per 1M input tokens and 830,926 toman per 1M output tokens; a 1,000-word request is around 1,350 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Nemotron 3 Ultra take?: Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 182,520 tokens.
Does Nemotron 3 Ultra 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.