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Muse Wonder API: toman pricing and code

meta/muse-wonder

Image inputTool callingReasoningJSON outputFile inputAudio input
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Input · per 1M tokens
1,384,877 toman
Output · per 1M tokens
6,924,387 toman
One 1,000-word request ≈
10,802 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 69,244 toman per 1M tokens. Web search: 865.55 toman per request.Pricing and top-ups

Muse Wonder 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/muse-wonder",
    "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/muse-wonder",
    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/muse-wonder",
  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 Muse Wonder good for?

Muse Wonder is one of Meta's models. Put "meta/muse-wonder" in the model field and the rest of your code stays as it is. Muse Wonder keeps 1,048,576 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 943,718 tokens. On price it sits in the "expensive" band — cheaper than 25 and dearer than 369 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 accepts audio input; 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 10,802 toman per 1,000-word exchange (1,384,877 in, 6,924,387 out, per 1M tokens). It supports cached input: repeated context is billed at 69,244 toman per 1M, which matters a lot if your system prompt is long. 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 Claude Opus 5.5, and Muse Wonder costs about the same, 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 9 thousand-word requests on Muse Wonder, and every 1,000 toman is about 93 words of round trip. A job with one million input tokens and one million output tokens comes to 8,309,264 toman in total. Filling this model's 1,048,576-token window costs 1,452,149 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "meta/muse-spark-1.1", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 4.4× more expensive (10,802 against 2,475 toman).

Meta has 6 models in our catalogue; the cheapest is Muse Glimmer 30B at 675 toman per thousand words and the dearest Muse Wonder at 10,802. Among the less common parameters it accepts reasoning_effort, repetition_penalty, top_k — all through the standard request body. It does not support seed, 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: work where the quality of the answer matters more than its cost, 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

Muse Wonder is a frontier reasoning model from Meta, designed for complex personal and professional agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with...

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 out4,847 toman
Summarising a ten-page document4,000 in + 600 out9,694 toman
Classifying a thousand short rows120,000 in + 20,000 out304,673 toman

Frequently asked

How do I call Muse Wonder 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/muse-wonder". Nothing else in your code changes.
What does Muse Wonder cost in toman?
1,384,877 toman per 1M input tokens and 6,924,387 toman per 1M output tokens; a 1,000-word request is around 10,802 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Muse Wonder take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 943,718 tokens.
Does Muse Wonder 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.