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Llama 4 Scout API: toman pricing and code

meta-llama/llama-4-scout

Image inputTool callingJSON output
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Input · per 1M tokens
34,622 toman
Output · per 1M tokens
103,866 toman
One 1,000-word request ≈
180 toman

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

Llama 4 Scout 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": "meta-llama/llama-4-scout",
    "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="meta-llama/llama-4-scout",
    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: "meta-llama/llama-4-scout",
  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 Llama 4 Scout good for?

Llama 4 Scout comes from Meta Llama; in uttapen you reach it with the model id "meta-llama/llama-4-scout". It accepts up to 327,680 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 16,384 tokens. On price it sits in the "very cheap" band — cheaper than 327 and dearer than 62 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; it returns schema-valid JSON through response_format, ready to hand to your code. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.

Pricing is 34,622 toman per 1M input tokens and 103,866 per 1M output tokens. A 1,000-word round trip on Llama 4 Scout lands near 180 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 Seed 1.6 Flash: Llama 4 Scout works out about 10% more expensive, 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 556 thousand-word requests on Llama 4 Scout, and every 1,000 toman is about 5,556 words of round trip. A job with one million input tokens and one million output tokens comes to 138,488 toman in total. Filling this model's 327,680-token window costs 11,345 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "meta-llama/llama-4-maverick", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 2.1× cheaper (180 against 378 toman). The context windows differ too: 327,680 against 524,288 tokens. On parameters, this one takes min_p while the other takes logprobs, response_format, top_logprobs.

Meta Llama has 8 models in our catalogue; the cheapest is Llama 3.1 8B Instruct at 59 toman per thousand words and the dearest Llama 3.1 70B Instruct at 648. Among the less common parameters it accepts logit_bias, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. It does not support include_reasoning, reasoning, response_format, 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 Nova Lite 1.0 at 300,000 tokens.

Good fits: high-volume work such as classification, tagging and bulk summarising, 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

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...

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 out93 toman
Summarising a ten-page document4,000 in + 600 out201 toman
Classifying a thousand short rows120,000 in + 20,000 out6,232 toman

Price history

DateInput (toman/1M)Output (toman/1M)
16 Oct 202634,622103,866
12 Oct 202662,319204,269
7 Sep 202634,622103,866

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

Frequently asked

How do I call Llama 4 Scout 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 "meta-llama/llama-4-scout". Nothing else in your code changes.
What does Llama 4 Scout cost in toman?
34,622 toman per 1M input tokens and 103,866 toman per 1M output tokens; a 1,000-word request is around 180 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Llama 4 Scout take?
Up to 327,680 tokens per request, roughly 246k words. A single answer can reach 16,384 tokens.
Does Llama 4 Scout 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.