Compare models

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

Muse Spark OpenMeta Remove
mai-ds-r1:freeNot in the catalogue Remove
Gemini 4 ArgonGoogle Remove
Perceptron Mk1.5Perceptron Remove

4 is the maximum. Remove one to add another.

muse-spark-open vs mai-ds-r1:free vs gemini-4-argon vs perceptron-mk1.5
Attribute	Muse Spark Openmuse-spark-open	mai-ds-r1:freeNot in the catalogue	Gemini 4 Argongemini-4-argon	Perceptron Mk1.5perceptron-mk1.5
Pricing
Input	$1.25 / 1M	—	$2.00 / 1M	$0.15 / 1M
Output	$4.25 / 1M	—	$10.00 / 1M	$1.50 / 1M
Cache Write (5m)	$1.25 / 1M	—	$2.00 / 1M	$0.15 / 1M
Cache Write (1h)	$1.25 / 1M	—	$2.00 / 1M	$0.15 / 1M
Cache Read	$1.25 / 1M	—	$2.00 / 1M	$0.15 / 1M
Web Search	$0 / 1M	—	$0 / 1M	$0 / 1M
Context
Max context	1M	—	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 Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.	—	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.