Nova 2 Lite API: toman pricing and code

amazon/nova-2-lite-v1

Image inputTool callingReasoningFile input
Input · per 1M tokens
103,866 toman
Output · per 1M tokens
865,548 toman
One 1,000-word request ≈
1,260 toman

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

Nova 2 Lite 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-2-lite-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-2-lite-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-2-lite-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 2 Lite good for?

Nova 2 Lite comes from Amazon; in uttapen you reach it with the model id "amazon/nova-2-lite-v1". It accepts up to 1,000,000 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 65,535 tokens. On price it sits in the "mid-range" band — cheaper than 160 and dearer than 223 of the other paid models in the catalogue.

Capabilities available on this id: it reads images directly, which makes it a real option for invoices, forms and screenshots; it takes files such as PDFs as input; it supports tool calling, so it can invoke your own functions with valid arguments; 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 1,260 toman per 1,000-word exchange (103,866 in, 865,548 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 Seed 2.1 Turbo: Nova 2 Lite 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 79 thousand-word requests on Nova 2 Lite, and every 1,000 toman is about 794 words of round trip. A job with one million input tokens and one million output tokens comes to 969,414 toman in total. Filling this model's 1,000,000-token window costs 103,866 toman on the input side alone, which is the real reason to keep conversation history short.

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 response_format, seed, structured_outputs, 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 Claude Fable 5 at 1,000,000 tokens.

What to use it for: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, 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

Nova 2 Lite is a fast, cost-effective reasoning model for everyday workloads that can process text, images, and videos to generate text. Nova 2 Lite demonstrates standout capabilities in processing...

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 out502 toman
Summarising a ten-page document4,000 in + 600 out935 toman
Classifying a thousand short rows120,000 in + 20,000 out29,775 toman

Frequently asked

How do I call Nova 2 Lite 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-2-lite-v1". Nothing else in your code changes.
What does Nova 2 Lite cost in toman?
103,866 toman per 1M input tokens and 865,548 toman per 1M output tokens; a 1,000-word request is around 1,260 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Nova 2 Lite take?
Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 65,535 tokens.
Does Nova 2 Lite 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.