Llama 4 Maverick API: toman pricing and code
meta-llama/llama-4-maverick
visiontoolsjsonYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
Llama 4 Maverick 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-maverick",
"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-maverick",
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-maverick",
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 Maverick good for?
The id for Llama 4 Maverick in our API is "meta-llama/llama-4-maverick", served from Meta. Context is 1,048,576 tokens; past that you have to summarise the history yourself. A single response can run to 115,200 tokens. On price it sits in the "cheap" band — cheaper than 111 and dearer than 295 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 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.
For a back-of-envelope figure: about 338 toman per 1,000-word exchange (58,025 in, 201,927 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 DeepSeek V4 Flash Vision Exp: Llama 4 Maverick works out roughly 1× more expensive, and its context window is the same size. 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 296 thousand-word requests on Llama 4 Maverick, and every 1,000 toman is about 2,959 words of round trip. A job with one million input tokens and one million output tokens comes to 259,952 toman in total. Filling this model's 1,048,576-token window costs 60,844 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-scout", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 2.2× dearer (338 against 151 toman). The context windows differ too: 1,048,576 against 1,310,720 tokens. Maximum answer length differs as well: 115,200 against 16,384 tokens. On parameters, this one takes logprobs, top_logprobs.
Meta has 8 models in our catalogue; the cheapest is Llama 3.1 8B Instruct at 49 toman per thousand words and the dearest Llama 4 Maverick at 338. Among the less common parameters it accepts logit_bias, logprobs, min_p, repetition_penalty, top_k, top_logprobs — all through the standard request body. It does not support include_reasoning, reasoning, 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 DeepSeek V4 Flash 0423 at 1,048,576 tokens.
What to use it for: 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.
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...
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 | 168 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 353 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 11,002 toman |
Frequently asked
- How do I call Llama 4 Maverick 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-maverick". Nothing else in your code changes.
- What does Llama 4 Maverick cost in toman?
- 58,025 toman per 1M input tokens and 201,927 toman per 1M output tokens; a 1,000-word request is around 338 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Llama 4 Maverick take?
- Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 115,200 tokens.
- Does Llama 4 Maverick 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.