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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 Spark 1.3MetaRemove
  3. mai-ds-r1:freeNot in the catalogueRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs muse-spark-1.3 vs mai-ds-r1:free vs gemini-4-argon
AttributePerceptron Mk1.5perceptron-mk1.5Muse Spark 1.3muse-spark-1.3mai-ds-r1:freeNot in the catalogueGemini 4 Argongemini-4-argon
Pricing
Input$0.15 / 1M$1.25 / 1M—$2.00 / 1M
Output$1.50 / 1M$4.25 / 1M—$10.00 / 1M
Cache Write (5m)$0.15 / 1M$1.25 / 1M—$2.00 / 1M
Cache Write (1h)$0.15 / 1M$1.25 / 1M—$2.00 / 1M
Cache Read$0.15 / 1M$1.25 / 1M—$2.00 / 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
ProviderPerceptronMeta—Google
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.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.—Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.