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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.

  1. mai-ds-r1:freeNot in the catalogueRemove
  2. Muse WonderMetaRemove
  3. Gemini 3.5 TranscribeGoogleRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs muse-wonder vs gemini-3.5-transcribe vs gemini-4-argon
Attributemai-ds-r1:freeNot in the catalogueMuse Wondermuse-wonderGemini 3.5 Transcribegemini-3.5-transcribeGemini 4 Argongemini-4-argon
Pricing
Input—$4.00 / 1M— Not priced per input token$2.00 / 1M
Output—$20.00 / 1M— Not priced per output token$10.00 / 1M
Cache Write (5m)—$4.00 / 1MNot applicable$2.00 / 1M
Cache Write (1h)—$4.00 / 1MNot applicable$2.00 / 1M
Cache Read—$4.00 / 1MNot applicable$2.00 / 1M
Web Search—$0 / 1M$0 / 1M$0 / 1M
Context
Max context—1M98.3K1M
Max output—N/AN/AN/A
Capabilities
Vision—YesYesYes
Function Calling—YesYesYes
JSON Mode—YesNoYes
Streaming—YesNoYes
Catalogue
Provider—MetaGoogleGoogle
Category—chatvoicechat
Charge type—Pay As You GoPay As You GoPay As You Go
Released——2026-09-25—
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.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.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.