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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. mai-ds-r1:freeNot in the catalogueRemove
  2. GLM 5.3 FlashZ.AIRemove
  3. Muse WonderMetaRemove
  4. Gemini 3.5 TranscribeGoogleRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs glm-5.3-flash vs muse-wonder vs gemini-3.5-transcribe
Attributemai-ds-r1:freeNot in the catalogueGLM 5.3 Flashglm-5.3-flashMuse Wondermuse-wonderGemini 3.5 Transcribegemini-3.5-transcribe
Pricing
Input—$0.075 / 1M$4.00 / 1M— Not priced per input token
Output—$0.25 / 1M$20.00 / 1M— Not priced per output token
Cache Write (5m)—$0.075 / 1M$4.00 / 1MNot applicable
Cache Write (1h)—$0.075 / 1M$4.00 / 1MNot applicable
Cache Read—$0.075 / 1M$4.00 / 1MNot applicable
Web Search—$0 / 1M$0 / 1M$0 / 1M
Context
Max context—1M1M98.3K
Max output—N/AN/AN/A
Capabilities
Vision—YesYesYes
Function Calling—YesYesYes
JSON Mode—YesYesNo
Streaming—YesYesNo
Catalogue
Provider—Z.AIMetaGoogle
Category—chatchatvoice
Charge type—Pay As You GoPay As You GoPay As You Go
Released———2026-09-25
Description
Summary—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.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.