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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
Gemini 4 ArgonGoogle Remove
Perceptron Mk1.5Perceptron 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 gemini-4-argon vs perceptron-mk1.5
Attribute	Muse Glimmer 30Bmuse-glimmer-30b	Gemini 4 Argongemini-4-argon	Perceptron Mk1.5perceptron-mk1.5
Pricing
Input	$0.35 / 1M	$2.00 / 1M	$0.15 / 1M
Output	$1.50 / 1M	$10.00 / 1M	$1.50 / 1M
Cache Write (5m)	$0.35 / 1M	$2.00 / 1M	$0.15 / 1M
Cache Write (1h)	$0.35 / 1M	$2.00 / 1M	$0.15 / 1M
Cache Read	$0.35 / 1M	$2.00 / 1M	$0.15 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	131K	1M	36.9K
Max output	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	No
Function Calling	Yes	Yes	No
JSON Mode	Yes	Yes	No
Streaming	Yes	Yes	Yes
Catalogue
Provider	Meta	Google	Perceptron
Category	chat	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	—	2026-09-25
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.	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.	Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.