Nova Pro 1.0 API: toman pricing and code

amazon/nova-pro-v1

Image inputTool calling
Input · per 1M tokens
276,975 toman
Output · per 1M tokens
1,107,902 toman
One 1,000-word request ≈
1,800 toman

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

Nova Pro 1.0 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": "amazon/nova-pro-v1",
    "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="amazon/nova-pro-v1",
    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: "amazon/nova-pro-v1",
  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 Nova Pro 1.0 good for?

Nova Pro 1.0 comes from Amazon; in uttapen you reach it with the model id "amazon/nova-pro-v1". It accepts up to 300,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 5,120 tokens. On price it sits in the "mid-range" band — cheaper than 144 and dearer than 249 of the other paid models in the catalogue.

What it can do beyond plain text: 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. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.

Pricing is 276,975 toman per 1M input tokens and 1,107,902 per 1M output tokens. A 1,000-word round trip on Nova Pro 1.0 lands near 1,800 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 Pareto 26.10, and Nova Pro 1.0 costs about the same, and its context window is smaller. 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 56 thousand-word requests on Nova Pro 1.0, and every 1,000 toman is about 556 words of round trip. A job with one million input tokens and one million output tokens comes to 1,384,877 toman in total. Filling this model's 300,000-token window costs 83,093 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "amazon/nova-lite-v1", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 13× more expensive (1,800 against 135 toman).

Amazon has 5 models in our catalogue; the cheapest is Nova Micro 1.0 at 79 toman per thousand words and the dearest Nova Premier 1.0 at 6,751. Among the less common parameters it accepts top_k — all through the standard request body. It does not support include_reasoning, reasoning, response_format, tool_choice, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is Llama 4 Scout at 327,680 tokens.

Good fits: product chatbots and internal assistants, where cost and quality have to balance, pulling text and fields out of images, agents that reach out to APIs and databases, analysing a long document or codebase in one request.

Provider's own description

Amazon Nova Pro 1.0 is a capable multimodal model from Amazon focused on providing a combination of accuracy, speed, and cost for a wide range of tasks. As of December...

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 out859 toman
Summarising a ten-page document4,000 in + 600 out1,773 toman
Classifying a thousand short rows120,000 in + 20,000 out55,395 toman

Frequently asked

How do I call Nova Pro 1.0 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 "amazon/nova-pro-v1". Nothing else in your code changes.
What does Nova Pro 1.0 cost in toman?
276,975 toman per 1M input tokens and 1,107,902 toman per 1M output tokens; a 1,000-word request is around 1,800 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Nova Pro 1.0 take?
Up to 300,000 tokens per request, roughly 225k words. A single answer can reach 5,120 tokens.
Does Nova Pro 1.0 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.