Nemotron 3.5 Super VL (free) API: toman pricing and code

nvidia/nemotron-3.5-super-vl:free

Free · shared capacityImage inputTool callingReasoningJSON output
Input · per 1M tokens
0 toman
Output · per 1M tokens
0 toman
One 1,000-word request ≈
0 toman

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

Nemotron 3.5 Super VL (free) 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-super-vl:free",
    "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"}}
      ]
    }]
  }'
import base64, json
from openai import OpenAI

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

img = base64.b64encode(open("factor.jpg", "rb").read()).decode()

resp = client.chat.completions.create(
    model="nvidia/nemotron-3.5-super-vl:free",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Return the invoice number, the date and the total as JSON."},
            {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img}"}},
        ],
    }],
    response_format={"type": "json_object"},
)
print(json.loads(resp.choices[0].message.content))
import OpenAI from "openai";
import { readFileSync } from "node:fs";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const img = readFileSync("factor.jpg").toString("base64");

const resp = await client.chat.completions.create({
  model: "nvidia/nemotron-3.5-super-vl:free",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Return the invoice number, the date and the total as JSON." },
      { type: "image_url", image_url: { url: `data:image/jpeg;base64,${img}` } },
    ],
  }],
  response_format: { type: "json_object" },
});
console.log(JSON.parse(resp.choices[0].message.content));

What is Nemotron 3.5 Super VL (free) good for?

Nemotron 3.5 Super VL (free) is one of NVIDIA's models. Put "nvidia/nemotron-3.5-super-vl:free" in the model field and the rest of your code stays as it is. Nemotron 3.5 Super VL (free) keeps 1,000,000 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 131,072 tokens. It is free: nothing leaves your wallet. The capacity is shared across all users and there is a daily cap per account, so treat it as a testing lane rather than a production dependency.

Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots; 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 0 toman per 1,000-word exchange (0 in, 0 out, per 1M tokens). 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 Dots3-Note Preview (free), 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.

This id gets confused with "nvidia/nemotron-3.5-super-vl", because the underlying model is the same. The difference is the suffix: "free" against the standard variant. The context windows differ too: 1,000,000 against 1,048,576 tokens. Maximum answer length differs as well: 131,072 against 943,718 tokens. On parameters the other 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. By context size the nearest option from another provider is Nova 2 Lite at 1,000,000 tokens.

What to use it for: proving an idea works before you spend anything, logic puzzles and code review, pulling text and fields out of images, 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 Super VL is an open multimodal mixture-of-experts model from NVIDIA, with 12B active parameters out of 120B total. It takes text, images, documents, GUI screenshots, and video...

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 out0 toman
Summarising a ten-page document4,000 in + 600 out0 toman
Classifying a thousand short rows120,000 in + 20,000 out0 toman

Other variants of this model

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

VariantModel idOutput / 1MContext
standardnvidia/nemotron-3.5-super-vl173,1101,048,576

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 Super VL (free) 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-super-vl:free". Nothing else in your code changes.
What does Nemotron 3.5 Super VL (free) cost in toman?
0 toman per 1M input tokens and 0 toman per 1M output tokens; a 1,000-word request is around 0 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Nemotron 3.5 Super VL (free) take?
Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 131,072 tokens.
Does Nemotron 3.5 Super VL (free) support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Image input is accepted through image_url, as a data URI or a public URL. Structured output through response_format works too.
What are the limits on a free model?
Free models have a daily per-user cap and their capacity is shared with everyone else. When the shared pool is exhausted the request is refused with a clear error and your wallet is untouched.