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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. Transcribe 1 ProFish AudioRemove
  2. Grok 4.6xAIRemove
  3. Muse WonderMetaRemove
transcribe-1-pro vs grok-4.6 vs muse-wonder
AttributeTranscribe 1 Protranscribe-1-proGrok 4.6grok-4.6Muse Wondermuse-wonder
Pricing
Input— Not priced per input token$2.00 / 1M$4.00 / 1M
Output— Not priced per output token$6.00 / 1M$20.00 / 1M
Cache Write (5m)Not applicable$2.00 / 1M$4.00 / 1M
Cache Write (1h)Not applicable$2.00 / 1M$4.00 / 1M
Cache ReadNot applicable$2.00 / 1M$4.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A500K1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderFish AudioxAIMeta
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-24——
Description
SummaryFish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.Grok 4.6 is SpaceXAI's smartest frontier model, delivering top-tier performance across coding, knowledge work, and STEM reasoning. It is designed for demanding technical and professional workloads that require strong problem solving, accurate instruction following, and reliable execution. Optimized for software engineering, scientific analysis, and complex knowledge tasks, Grok 4.6 is well suited for advanced coding, research, and agentic workflows where high capability and reasoning quality are critical.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.