Muse Spark 1.3 Contributor API: toman pricing and code
meta/muse-spark-1.3-contributor
visiontoolsreasoningjsonfilesaudioYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 580 toman / 1M. Web search: 725.31 toman / request.Pricing and top-ups
Muse Spark 1.3 Contributor 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-1.3-contributor",
"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-1.3-contributor",
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-1.3-contributor",
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 1.3 Contributor good for?
The id for Muse Spark 1.3 Contributor in our API is "meta/muse-spark-1.3-contributor", served from Meta. Context is 1,048,576 tokens; past that you have to summarise the history yourself. A single response can run to 943,718 tokens. On price it sits in the "very cheap" band — cheaper than 33 and dearer than 373 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 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.
Pricing is 29,013 toman per 1M input tokens and 58,025 per 1M output tokens. A 1,000-word round trip on Muse Spark 1.3 Contributor lands near 113 toman. It supports cached input: repeated context is billed at 580 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 Gemini 2.5 Flash Lite (batch): Muse Spark 1.3 Contributor works out roughly 1.2× 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 885 thousand-word requests on Muse Spark 1.3 Contributor, and every 1,000 toman is about 8,850 words of round trip. A job with one million input tokens and one million output tokens comes to 87,038 toman in total. Filling this model's 1,048,576-token window costs 30,422 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-contributor", and choosing between those two is where most people hesitate. Both land at nearly the same price on a thousand-word request (113 and 113 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.
Good fits: high-volume work such as classification, tagging and bulk summarising, 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 1.3 Contributor is the cost-efficient contributor tier of Meta’s multimodal reasoning model for experimentation, learning, and early-stage agentic, multi-agent, and coding workflows. It is designed to track information...
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 | 67 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 151 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 4,642 toman |
Frequently asked
- How do I call Muse Spark 1.3 Contributor 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-1.3-contributor". Nothing else in your code changes.
- What does Muse Spark 1.3 Contributor cost in toman?
- 29,013 toman per 1M input tokens and 58,025 toman per 1M output tokens; a 1,000-word request is around 113 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does Muse Spark 1.3 Contributor take?
- Up to 1,048,576 tokens per request, roughly 786k words. A single answer can reach 943,718 tokens.
- Does Muse Spark 1.3 Contributor 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.