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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. GLM 5.3 FlashZ.AIRemove
  3. Gemini 3.5 TranscribeGoogleRemove
  4. Hy4 previewTencentRemove

4 is the maximum. Remove one to add another.

muse-wonder vs glm-5.3-flash vs gemini-3.5-transcribe vs hy4-preview
AttributeMuse Wondermuse-wonderGLM 5.3 Flashglm-5.3-flashGemini 3.5 Transcribegemini-3.5-transcribeHy4 previewhy4-preview
Pricing
Input$4.00 / 1M$0.075 / 1M— Not priced per input token$0.834 / 1M
Output$20.00 / 1M$0.25 / 1M— Not priced per output token$2.50 / 1M
Cache Write (5m)$4.00 / 1M$0.075 / 1MNot applicable$0.834 / 1M
Cache Write (1h)$4.00 / 1M$0.075 / 1MNot applicable$0.834 / 1M
Cache Read$4.00 / 1M$0.075 / 1MNot applicable$0.834 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M98.3K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesNoYes
StreamingYesYesNoYes
Catalogue
ProviderMetaZ.AIGoogleTencent
Categorychatchatvoicechat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released——2026-09-25—
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.GLM-5.3-Flash is Z.AI's efficient native multimodal model, designed for coding and long-horizon agentic workflows. It combines strong multimodal capabilities with an architecture optimized for responsive, cost-efficient task execution. Built on a hybrid sparse and linear attention architecture, GLM-5.3-Flash maintains accurate long-context behavior while reducing computational overhead, making it well suited for coding agents, extended multi-step tasks, and scalable production workloads.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.Tencent Hy4 Preview is a Mixture-of-Experts (MoE) model from Tencent, featuring 770B total parameters with 49B activated per token. It is designed for coding agents, complex tool-driven workflows, and professional productivity tasks that require strong planning and reliable execution. Optimized for context continuity and sustained multi-step work, Hy4 Preview is well suited for long-horizon coding, agentic automation, tool orchestration, and complex real-world workflows.