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Muse Glimmer 30B API: toman pricing and code

meta/muse-glimmer-30b

Image inputTool callingReasoningJSON output
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Input · per 1M tokens
103,866 toman
Output · per 1M tokens
415,463 toman
One 1,000-word request ≈
675 toman

You are billed for the usage the request actually reported. Prices follow the market. Cached input: 13,849 toman per 1M tokens.Pricing and top-ups

Muse Glimmer 30B 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-glimmer-30b",
    "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-glimmer-30b",
    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-glimmer-30b",
  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 Glimmer 30B good for?

Meta publishes this model; we expose it under the id "meta/muse-glimmer-30b". Its context window is 131,072 tokens, roughly 98k English words in one request. A single response can run to 16,384 tokens. On price it sits in the "cheap" band — cheaper than 232 and dearer than 157 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; 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 675 toman per 1,000-word exchange (103,866 in, 415,463 out, per 1M tokens). It supports cached input: repeated context is billed at 13,849 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 MiniMax M3, and Muse Glimmer 30B costs about the same, and its context window is smaller. 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 148 thousand-word requests on Muse Glimmer 30B, and every 1,000 toman is about 1,481 words of round trip. A job with one million input tokens and one million output tokens comes to 519,329 toman in total. Filling this model's 131,072-token window costs 13,614 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 3.7× cheaper (675 against 2,475 toman). The context windows differ too: 131,072 against 1,048,576 tokens. Maximum answer length differs as well: 16,384 against 943,718 tokens. On parameters, this one takes frequency_penalty, logit_bias, min_p, presence_penalty.

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 logit_bias, min_p, presence_penalty, reasoning_effort, repetition_penalty, top_k — all through the standard request body. By context size the nearest option from another provider is Aion-2.0 at 131,072 tokens.

What to use it for: 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.

Provider's own description

Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It is suited for long-horizon...

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 out322 toman
Summarising a ten-page document4,000 in + 600 out665 toman
Classifying a thousand short rows120,000 in + 20,000 out20,773 toman

Price history

DateInput (toman/1M)Output (toman/1M)
6 Oct 2026103,866415,463
30 Sep 2026121,177519,329
20 Sep 2026103,866415,463
18 Sep 2026121,177519,329
17 Sep 2026103,866380,841
13 Sep 2026121,177519,329
7 Sep 2026103,866380,841

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call Muse Glimmer 30B 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-glimmer-30b". Nothing else in your code changes.
What does Muse Glimmer 30B cost in toman?
103,866 toman per 1M input tokens and 415,463 toman per 1M output tokens; a 1,000-word request is around 675 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Muse Glimmer 30B take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 16,384 tokens.
Does Muse Glimmer 30B 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.