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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

Muse Glimmer 30BMeta Remove
Universal-3.5 ProAssemblyAI Remove
Gemini 4 ArgonGoogle Remove

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DeepSeek V4.1 Flash 1017
Nemotron 3.5 Super VL (Free)
Nemotron 3.5 Super VL
Muse Spark Open
Muse Wonder
Gemini 3.1 Flash Lite Image (Google Vertex AI)
Gemini 3.1 Flash Lite Image (Google AI Studio)Enterprise & partner access
Gemini 3.1 Flash Image (Google Vertex AI)Enterprise & partner access

muse-glimmer-30b vs universal-3-5-pro vs gemini-4-argon
Attribute	Muse Glimmer 30Bmuse-glimmer-30b	Universal-3.5 Prouniversal-3-5-pro	Gemini 4 Argongemini-4-argon
Pricing
Input	$0.35 / 1M	— Not priced per input token	$2.00 / 1M
Output	$1.50 / 1M	— Not priced per output token	$10.00 / 1M
Cache Write (5m)	$0.35 / 1M	Not applicable	$2.00 / 1M
Cache Write (1h)	$0.35 / 1M	Not applicable	$2.00 / 1M
Cache Read	$0.35 / 1M	Not applicable	$2.00 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	131K	N/A	1M
Max output	N/A	N/A	N/A
Capabilities
Vision	Yes	No	Yes
Function Calling	Yes	No	Yes
JSON Mode	Yes	No	Yes
Streaming	Yes	No	Yes
Catalogue
Provider	Meta	AssemblyAI	Google
Category	chat	voice	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	2026-09-22	—
Description
Summary	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 combines strong multi-step reasoning, reliable tool use, failure recovery, image understanding, and multilingual support across 100+ languages. Designed for long-horizon agentic and coding workflows, Muse Glimmer 30B offers a practical balance of capability and deployment efficiency, making it well suited for local coding assistants, multimodal agents, and production workflows that require sustained autonomous execution.	AssemblyAI Universal-3.5 Pro speech-to-text. Billed per second of audio.	Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.