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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. GPT-6 AstraOpenAIRemove
  3. mai-ds-r1:freeNot in the catalogueRemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs gpt-6-astra vs mai-ds-r1:free vs muse-wonder
AttributePerceptron Mk1.5perceptron-mk1.5GPT-6 Astragpt-6-astramai-ds-r1:freeNot in the catalogueMuse 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 context36.9K1M—1M
Max outputN/AN/A—N/A
Capabilities
VisionNoYes—Yes
Function CallingNoYes—Yes
JSON ModeNoYes—Yes
StreamingYesYes—Yes
Catalogue
ProviderPerceptronOpenAI—Meta
Categorychatchat—chat
Charge typePay As You GoPay As You Go—Pay As You Go
Released2026-09-25———
Description
SummaryPerceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.GPT-6 Astra is OpenAI's flagship model for demanding end-to-end professional work, designed for advanced analysis, software engineering, deep research, scientific tasks, and document creation. It is particularly strong in long-horizon agentic workflows, including tasks that require sustained reasoning, tool orchestration, and computer and browser use, making it well suited for complex autonomous workflows and production-grade knowledge work.—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.