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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. GLM 5.3Z.AIRemove
  2. GLM 5.3 FlashZ.AIRemove
  3. Muse WonderMetaRemove
  4. Transcribe 1 ProFish AudioRemove

4 is the maximum. Remove one to add another.

glm-5.3 vs glm-5.3-flash vs muse-wonder vs transcribe-1-pro
AttributeGLM 5.3glm-5.3GLM 5.3 Flashglm-5.3-flashMuse Wondermuse-wonderTranscribe 1 Protranscribe-1-pro
Pricing
Input$1.40 / 1M$0.075 / 1M$4.00 / 1M— Not priced per input token
Output$4.40 / 1M$0.25 / 1M$20.00 / 1M— Not priced per output token
Cache Write (5m)$1.40 / 1M$0.075 / 1M$4.00 / 1MNot applicable
Cache Write (1h)$1.40 / 1M$0.075 / 1M$4.00 / 1MNot applicable
Cache Read$1.40 / 1M$0.075 / 1M$4.00 / 1MNot applicable
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1MN/A
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesNo
Function CallingYesYesYesNo
JSON ModeYesYesYesNo
StreamingYesYesYesNo
Catalogue
ProviderZ.AIZ.AIMetaFish Audio
Categorychatchatchatvoice
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released———2026-09-24
Description
SummaryGLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.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.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.Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.