Muse Glimmer 30B

About

Muse Glimmer 30B is a 30B open-weight model from Meta. Meta Muse Glimmer 30B. Text, multi-image, and native video. It accepts text, images, and video. Context window is 128K. Released August 9, 2026.

Parameters
30B
Published parameter count.
Context window
128K
Tokens of context on a request.
License
Apache-2.0
License on the weights.

Pricing

Token rates are US dollars per 1M tokens. Image rates are per 1K images. Audio rates are per 1M audio seconds.

Rate	Price
Input / 1M	$0.35
Cached input / 1M	$0.04
Output / 1M	$1.50

Compare

Muse Glimmer 30B is one of the catalog's detection models. The table is each model's published price, size, and context.

Model	What it does	Price	Parameters	Context
Muse Glimmer 30B	Meta Muse Glimmer 30B. Text, multi-image, and native video.	$0.35 / 1M input	30B	128K
PP-OCRv6	PaddleOCR PP-OCRv6 medium text detection and recognition; scene OCR JSONL on image chat; text returns plain text; document_url fan-out via text.	$0.02 / 1M input	20M	—
Muse Spark 1.2	Meta Muse Spark 1.2. Text, multi-image, and native video. Reasoning model.	$1.25 / 1M input	—	1M
Gemini 3.5 Flash Lite	Fastest and cheapest Gemini tier. Multimodal chat; emits no reasoning tokens.	$0.30 / 1M input	—	1M

Benchmarks

Published scores for Muse Glimmer 30B, from the Qwen3.8 model card.

Evals

OmniDocBench 1.575.8
CharXiv (RQ)78.8

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	OmniDocBench 1.5: 75.8.
Cost and performance	Lower listed rate, mid-pack latency

Methods

Methods this model serves.

Method	Returns
detection	Boxes around objects
chat	The model's reply

Estimate cost

Estimate. An hour of video is 15 frames a minute at 256 tokens a frame, plus the audio in that request, and 500 output tokens a minute.

Hours of video

$165.96

Quick start

Call this model on the OpenAI-compatible gateway. The model id is already filled in.

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="meta/muse-glimmer-30b",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

print(response.choices[0].message.content)