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 WonderMeta Remove
mai-ds-r1:freeNot in the catalogue Remove
Gemini 4 ArgonGoogle Remove
Transcribe 1 ProFish Audio Remove

4 is the maximum. Remove one to add another.

muse-wonder vs mai-ds-r1:free vs gemini-4-argon vs transcribe-1-pro
Attribute	Muse Wondermuse-wonder	mai-ds-r1:freeNot in the catalogue	Gemini 4 Argongemini-4-argon	Transcribe 1 Protranscribe-1-pro
Pricing
Input	$4.00 / 1M	—	$2.00 / 1M	— Not priced per input token
Output	$20.00 / 1M	—	$10.00 / 1M	— Not priced per output token
Cache Write (5m)	$4.00 / 1M	—	$2.00 / 1M	Not applicable
Cache Write (1h)	$4.00 / 1M	—	$2.00 / 1M	Not applicable
Cache Read	$4.00 / 1M	—	$2.00 / 1M	Not applicable
Web Search	$0 / 1M	—	$0 / 1M	$0 / 1M
Context
Max context	1M	—	1M	N/A
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	No
Catalogue
Provider	Meta	—	Google	Fish Audio
Category	chat	—	chat	voice
Charge type	Pay As You Go	—	Pay As You Go	Pay As You Go
Released	—	—	—	2026-09-24
Description
Summary	Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision.	—	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.	Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <\|speaker:0\|>. Billed per second of audio.