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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. Perceptron Mk1.5PerceptronRemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs glm-5.3-flash vs perceptron-mk1.5 vs muse-wonder
Attributemai-ds-r1:freeNot in the catalogueGLM 5.3 Flashglm-5.3-flashPerceptron Mk1.5perceptron-mk1.5Muse Wondermuse-wonder
Pricing
Input—$0.075 / 1M$0.15 / 1M$4.00 / 1M
Output—$0.25 / 1M$1.50 / 1M$20.00 / 1M
Cache Write (5m)—$0.075 / 1M$0.15 / 1M$4.00 / 1M
Cache Write (1h)—$0.075 / 1M$0.15 / 1M$4.00 / 1M
Cache Read—$0.075 / 1M$0.15 / 1M$4.00 / 1M
Web Search—$0 / 1M$0 / 1M$0 / 1M
Context
Max context—1M36.9K1M
Max output—N/AN/AN/A
Capabilities
Vision—YesNoYes
Function Calling—YesNoYes
JSON Mode—YesNoYes
Streaming—YesYesYes
Catalogue
Provider—Z.AIPerceptronMeta
Category—chatchatchat
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.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.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.