Muse Spark 1.1 API: toman pricing and code
meta/muse-spark-1.1
visiontoolsreasoningjsonfilesaudioYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 43,519 toman / 1M. Web search: 725.31 toman / 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 275 and dearer than 131 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 2,074 toman per 1,000-word exchange (362,656 in, 1,233,031 out, per 1M tokens). It supports cached input: repeated context is billed at 43,519 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 o4 Mini: Muse Spark 1.1 works out roughly 1× 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 48 thousand-word requests on Muse Spark 1.1, and every 1,000 toman is about 482 words of round trip. A job with one million input tokens and one million output tokens comes to 1,595,688 toman in total. Filling this model's 1,048,576-token window costs 380,273 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 (2,074 and 2,074 toman).
Meta has 7 models in our catalogue; the cheapest is Muse Spark 1.2 Contributor at 113 toman per thousand words and the dearest Muse Spark 1.3 at 2,074. 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.
Muse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with a 1M-token context...
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 | 1,037 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 2,190 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 68,179 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?
- 362,656 toman per 1M input tokens and 1,233,031 toman per 1M output tokens; a 1,000-word request is around 2,074 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.