Skip to content

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. Gemini 4 ArgonGoogleRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs perceptron-mk1.5 vs gemini-4-argon vs muse-spark-1.3
Attributemai-ds-r1:freeNot in the cataloguePerceptron Mk1.5perceptron-mk1.5Gemini 4 Argongemini-4-argonMuse Spark 1.3muse-spark-1.3
Pricing
Input—$0.15 / 1M$2.00 / 1M$1.25 / 1M
Output—$1.50 / 1M$10.00 / 1M$4.25 / 1M
Cache Write (5m)—$0.15 / 1M$2.00 / 1M$1.25 / 1M
Cache Write (1h)—$0.15 / 1M$2.00 / 1M$1.25 / 1M
Cache Read—$0.15 / 1M$2.00 / 1M$1.25 / 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—PerceptronGoogleMeta
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.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.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.