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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. Muse Spark OpenMetaRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. Gemini 3.8 FlashGoogleRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

muse-spark-open vs perceptron-mk1.5 vs gemini-3.8-flash vs gemini-4-argon
AttributeMuse Spark Openmuse-spark-openPerceptron Mk1.5perceptron-mk1.5Gemini 3.8 Flashgemini-3.8-flashGemini 4 Argongemini-4-argon
Pricing
Input$1.25 / 1M$0.15 / 1M$0.75 / 1M$2.00 / 1M
Output$4.25 / 1M$1.50 / 1M$3.75 / 1M$10.00 / 1M
Cache Write (5m)$1.25 / 1M$0.15 / 1M$0.75 / 1M$2.00 / 1M
Cache Write (1h)$1.25 / 1M$0.15 / 1M$0.75 / 1M$2.00 / 1M
Cache Read$1.25 / 1M$0.15 / 1M$0.75 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M36.9K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesYesYesYes
Catalogue
ProviderMetaPerceptronGoogleGoogle
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released—2026-09-25——
Description
SummaryMuse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.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.