Muse Glimmer 30B API: toman pricing and code
meta/muse-glimmer-30b
visiontoolsreasoningjsonYou are billed for the usage the request actually reported. Prices follow the market. Cached input: 11,605 toman / 1M.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 117,964 tokens. On price it sits in the "cheap" band — cheaper than 146 and dearer than 260 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 528 toman per 1,000-word exchange (87,038 in, 319,138 out, per 1M tokens). It supports cached input: repeated context is billed at 11,605 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 Qwen3.6 Flash: Muse Glimmer 30B works out roughly 1.1× more expensive, 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 189 thousand-word requests on Muse Glimmer 30B, and every 1,000 toman is about 1,894 words of round trip. A job with one million input tokens and one million output tokens comes to 406,175 toman in total. Filling this model's 131,072-token window costs 11,408 toman on the input side alone, which is the real reason to keep conversation history short.
This id gets confused with "meta/muse-glimmer-30b:batch", because the underlying model is the same. The difference is the suffix: no suffix (the standard variant) against "batch". On a thousand-word request this variant works out 1.3× cheaper (528 against 698 toman). On parameters, this one takes logprobs, seed, top_logprobs.
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 logit_bias, logprobs, min_p, 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.
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.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 258 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 540 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 16,827 toman |
Other variants of this model
Same core model, different execution terms and different price. This page is the standard variant.
| Variant | Model id | Output / 1M | Context |
|---|---|---|---|
| batch (cheaper, slower) | meta/muse-glimmer-30b:batch | 435,188 | 131,072 |
Put the variant's id verbatim in the model field; nothing else in your code changes.
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?
- 87,038 toman per 1M input tokens and 319,138 toman per 1M output tokens; a 1,000-word request is around 528 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 117,964 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.