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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. Perceptron Mk1.5PerceptronRemove
  2. Muse WonderMetaRemove
  3. Muse Spark 1.3MetaRemove
  4. mai-ds-r1:freeNot in the catalogueRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs muse-wonder vs muse-spark-1.3 vs mai-ds-r1:free
AttributePerceptron Mk1.5perceptron-mk1.5Muse Wondermuse-wonderMuse Spark 1.3muse-spark-1.3mai-ds-r1:freeNot in the catalogue
Pricing
Input$0.15 / 1M$4.00 / 1M$1.25 / 1M—
Output$1.50 / 1M$20.00 / 1M$4.25 / 1M—
Cache Write (5m)$0.15 / 1M$4.00 / 1M$1.25 / 1M—
Cache Write (1h)$0.15 / 1M$4.00 / 1M$1.25 / 1M—
Cache Read$0.15 / 1M$4.00 / 1M$1.25 / 1M—
Web Search$0 / 1M$0 / 1M$0 / 1M—
Context
Max context36.9K1M1M—
Max outputN/AN/AN/A—
Capabilities
VisionNoYesYes—
Function CallingNoYesYes—
JSON ModeNoYesYes—
StreamingYesYesYes—
Catalogue
ProviderPerceptronMetaMeta—
Categorychatchatchat—
Charge typePay As You GoPay As You GoPay As You Go—
Released2026-09-25———
Description
SummaryPerceptron 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.Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.—