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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. mai-ds-r1:freeNot in the catalogueRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. GPT-6 Astra ProOpenAIRemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs perceptron-mk1.5 vs gpt-6-astra-pro vs muse-wonder
Attributemai-ds-r1:freeNot in the cataloguePerceptron Mk1.5perceptron-mk1.5GPT-6 Astra Progpt-6-astra-proMuse Wondermuse-wonder
Pricing
Input—$0.15 / 1M$10.00 / 1M$4.00 / 1M
Output—$1.50 / 1M$50.00 / 1M$20.00 / 1M
Cache Write (5m)—$0.15 / 1M$10.00 / 1M$4.00 / 1M
Cache Write (1h)—$0.15 / 1M$10.00 / 1M$4.00 / 1M
Cache Read—$0.15 / 1M$10.00 / 1M$4.00 / 1M
Web Search—$0 / 1M$0 / 1M$0 / 1M
Context
Max context—36.9K1M1M
Max output—N/AN/AN/A
Capabilities
Vision—NoYesYes
Function Calling—NoYesYes
JSON Mode—NoYesYes
Streaming—YesYesYes
Catalogue
Provider—PerceptronOpenAIMeta
Category—chatchatchat
Charge type—Pay As You GoPay As You GoPay As You Go
Released—2026-09-25——
Description
Summary—Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.GPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost.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.