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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. Gemini 4 ArgonGoogleRemove
  4. GLM 5Z.AIRemove

4 is the maximum. Remove one to add another.

muse-wonder vs transcribe-1-pro vs gemini-4-argon vs glm-5
AttributeMuse Wondermuse-wonderTranscribe 1 Protranscribe-1-proGemini 4 Argongemini-4-argonGLM 5glm-5
Pricing
Input$4.00 / 1M— Not priced per input token$2.00 / 1M$0.60 / 1M
Output$20.00 / 1M— Not priced per output token$10.00 / 1M$2.20 / 1M
Cache Write (5m)$4.00 / 1MNot applicable$2.00 / 1M$0.60 / 1M
Cache Write (1h)$4.00 / 1MNot applicable$2.00 / 1M$0.60 / 1M
Cache Read$4.00 / 1MNot applicable$2.00 / 1M$0.60 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1MN/A1M202.8K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderMetaFish AudioGoogleZ.AI
Categorychatvoicechatchat
Charge typePay As You GoPay As You GoPay As You GoPay 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.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.GLM-5 is Z.AI's flagship open-source foundation model, engineered for complex systems design and long-horizon agent workflows. Built with expert developers in mind, it delivers production-grade performance on large-scale programming tasks, rivaling leading closed-source models. With strong agentic planning, deep backend reasoning, and iterative self-correction capabilities, GLM-5 extends beyond traditional code generation to support full-system construction and autonomous execution, making it well suited for advanced engineering and agent-driven development environments.