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Muse Spark 1.1 API: toman pricing and code

meta/muse-spark-1.1

Image inputTool callingReasoningJSON outputFile input
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Input · per 1M tokens
432,774 toman
Output · per 1M tokens
1,471,432 toman
One 1,000-word request ≈
2,475 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 51,933 toman per 1M tokens. Web search: 865.55 toman per request.Pricing and top-ups

Muse Spark 1.1 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-spark-1.1",
    "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-spark-1.1",
    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-spark-1.1",
  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 Spark 1.1 good for?

Muse Spark 1.1 comes from Meta; in uttapen you reach it with the model id "meta/muse-spark-1.1". It accepts up to 1,048,576 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 943,718 tokens. On price it sits in the "mid-range" band — cheaper than 126 and dearer than 269 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 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 2,475 toman per 1,000-word exchange (432,774 in, 1,471,432 out, per 1M tokens). It supports cached input: repeated context is billed at 51,933 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 Inkling: Muse Spark 1.1 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 40 thousand-word requests on Muse Spark 1.1, and every 1,000 toman is about 404 words of round trip. A job with one million input tokens and one million output tokens comes to 1,904,206 toman in total. Filling this model's 1,048,576-token window costs 453,797 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-1.2", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (2,475 and 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: 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, extracting data against a fixed schema, analysing a long document or codebase in one request.

Provider's own description

Muse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks. It accepts text, images, video, and PDF documents and returns text, with a 1M-token context window....

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 out1,238 toman
Summarising a ten-page document4,000 in + 600 out2,614 toman
Classifying a thousand short rows120,000 in + 20,000 out81,362 toman

Frequently asked

How do I call Muse Spark 1.1 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-spark-1.1". Nothing else in your code changes.
What does Muse Spark 1.1 cost in toman?
432,774 toman per 1M input tokens and 1,471,432 toman per 1M output tokens; a 1,000-word request is around 2,475 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Muse Spark 1.1 take?
Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 943,718 tokens.
Does Muse Spark 1.1 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.