uttapen
Create an API key

Nemotron 3 Nano 30B A3B API: toman pricing and code

nvidia/nemotron-3-nano-30b-a3b

toolsreasoningjson
Create a keyTry it
Input · per 1M tokens
14,506 toman
Output · per 1M tokens
58,025 toman
One 1,000-word request ≈
94 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 8,704 toman / 1M.Pricing and top-ups

Nemotron 3 Nano 30B A3B 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-nano-30b-a3b",
    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 Nano 30B A3B good for?

NVIDIA publishes this model; we expose it under the id "nvidia/nemotron-3-nano-30b-a3b". Its context window is 262,144 tokens, roughly 197k English words in one request. A single response can run to 235,929 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 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 14,506 toman per 1M input tokens and 58,025 per 1M output tokens. A 1,000-word round trip on Nemotron 3 Nano 30B A3B lands near 94 toman. It supports cached input: repeated context is billed at 8,704 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 Qwen2.5 7B Instruct: Nemotron 3 Nano 30B A3B works out roughly 1.2× 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 1,064 thousand-word requests on Nemotron 3 Nano 30B A3B, and every 1,000 toman is about 10,638 words of round trip. A job with one million input tokens and one million output tokens comes to 72,531 toman in total. Filling this model's 262,144-token window costs 3,803 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "nvidia/nemotron-3.5-lightning", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (94 and 106 toman). Maximum answer length differs as well: 235,929 against 131,072 tokens.

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, repetition_penalty, top_k, top_logprobs — all through the standard request body. By context size the nearest option from another provider is Trinity Large Thinking at 262,144 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 Nano 30B A3B is a small language MoE model with highest compute efficiency and accuracy for developers to build specialized agentic AI systems. The model is fully...

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 out45 toman
Summarising a ten-page document4,000 in + 600 out93 toman
Classifying a thousand short rows120,000 in + 20,000 out2,901 toman

Frequently asked

How do I call Nemotron 3 Nano 30B A3B 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-nano-30b-a3b". Nothing else in your code changes.
What does Nemotron 3 Nano 30B A3B cost in toman?
14,506 toman per 1M input tokens and 58,025 toman per 1M output tokens; a 1,000-word request is around 94 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Nemotron 3 Nano 30B A3B take?
Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
Does Nemotron 3 Nano 30B A3B 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.