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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. Muse WonderMetaRemove
  2. Transcribe 1 ProFish AudioRemove
  3. mai-ds-r1:freeNot in the catalogueRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

muse-wonder vs transcribe-1-pro vs mai-ds-r1:free vs muse-spark-1.3
AttributeMuse Wondermuse-wonderTranscribe 1 Protranscribe-1-promai-ds-r1:freeNot in the catalogueMuse 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 context1MN/A—1M
Max outputN/AN/A—N/A
Capabilities
VisionYesNo—Yes
Function CallingYesNo—Yes
JSON ModeYesNo—Yes
StreamingYesNo—Yes
Catalogue
ProviderMetaFish Audio—Meta
Categorychatvoice—chat
Charge typePay As You GoPay As You Go—Pay As You Go
Released—2026-09-24——
Description
SummaryMuse 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.Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.—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.