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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.

  1. mai-ds-r1:freeNot in the catalogueRemove
  2. Muse WonderMetaRemove
  3. Gemini 3.5 TranscribeGoogleRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs muse-wonder vs gemini-3.5-transcribe vs muse-spark-1.3
Attributemai-ds-r1:freeNot in the catalogueMuse Wondermuse-wonderGemini 3.5 Transcribegemini-3.5-transcribeMuse Spark 1.3muse-spark-1.3
Pricing
Input—$4.00 / 1M— Not priced per input token$1.25 / 1M
Output—$20.00 / 1M— Not priced per output token$4.25 / 1M
Cache Write (5m)—$4.00 / 1MNot applicable$1.25 / 1M
Cache Write (1h)—$4.00 / 1MNot applicable$1.25 / 1M
Cache Read—$4.00 / 1MNot applicable$1.25 / 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—MetaGoogleMeta
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.Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.