How do I call Nemotron 3.5 Lightning 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-lightning". Nothing else in your code changes.

What does Nemotron 3.5 Lightning cost in toman?

24,235 toman per 1M input tokens and 69,244 toman per 1M output tokens; a 1,000-word request is around 122 toman. You pay for the usage that request actually reported, and a failed request costs nothing.

How much input does Nemotron 3.5 Lightning take?

Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.

Does Nemotron 3.5 Lightning 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 3.5 Lightning API: toman pricing and code

nvidia/nemotron-3.5-lightning

Tool callingReasoningJSON output

Create a key Try it

Input · per 1M tokens

24,235 toman

Output · per 1M tokens

69,244 toman

One 1,000-word request ≈

122 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 13,849 toman per 1M tokens.Pricing and top-ups

Nemotron 3.5 Lightning 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-3.5-lightning", 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-3.5-lightning", 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-3.5-lightning", 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-3.5-lightning", 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-3.5-lightning",
    "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 3.5 Lightning good for?

NVIDIA publishes this model; we expose it under the id "nvidia/nemotron-3.5-lightning". 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 355 and dearer than 35 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 122 toman per 1,000-word exchange (24,235 in, 69,244 out, per 1M tokens). It supports cached input: repeated context is billed at 13,849 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 3.5 Lightning works out about 9% 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 820 thousand-word requests on Nemotron 3.5 Lightning, and every 1,000 toman is about 8,197 words of round trip. A job with one million input tokens and one million output tokens comes to 93,479 toman in total. Filling this model's 262,144-token window costs 6,353 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-lightning: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: 235,929 against 65,536 tokens. On parameters, this one takes frequency_penalty, logit_bias, logprobs, min_p.

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, logprobs, min_p, presence_penalty, 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: 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 3.5 Lightning is an open mixture-of-experts model from NVIDIA, with 3B active parameters out of 30B total. It is suited for high-throughput agentic workloads and specialized tasks that...

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	64 toman
Summarising a ten-page document	4,000 in + 600 out	138 toman
Classifying a thousand short rows	120,000 in + 20,000 out	4,293 toman

Job

Tokens

Cost

One chat turn with a medium history

1,500 in + 400 out

64 toman

Summarising a ten-page document

4,000 in + 600 out

138 toman

Classifying a thousand short rows

120,000 in + 20,000 out

4,293 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-lightning:free	0	1,000,000

Variant

Model id

Output / 1M

Context

free

nvidia/nemotron-3.5-lightning: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)
10 Oct 2026	24,235	69,244
10 Oct 2026	16,238	46,393
10 Oct 2026	24,235	69,244
9 Oct 2026	16,238	46,393
7 Oct 2026	16,965	48,471
5 Oct 2026	20,773	55,395
3 Oct 2026	20,600	58,857
2 Oct 2026	20,773	55,395
1 Oct 2026	20,600	58,857
29 Sep 2026	20,773	55,395
26 Sep 2026	27,698	69,244
25 Sep 2026	24,235	69,244

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

Frequently asked

How do I call Nemotron 3.5 Lightning 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-lightning". Nothing else in your code changes.
What does Nemotron 3.5 Lightning cost in toman?: 24,235 toman per 1M input tokens and 69,244 toman per 1M output tokens; a 1,000-word request is around 122 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Nemotron 3.5 Lightning take?: Up to 262,144 tokens per request, roughly 197k words. A single answer can reach 235,929 tokens.
Does Nemotron 3.5 Lightning 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.