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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Gemini 3.5 Flash-LiteGoogleRemove
  2. Muse Spark OpenMetaRemove
  3. Gemini 4 ArgonGoogleRemove
gemini-3.5-flash-lite vs muse-spark-open vs gemini-4-argon
AttributeGemini 3.5 Flash-Litegemini-3.5-flash-liteMuse Spark Openmuse-spark-openGemini 4 Argongemini-4-argon
Pricing
Input$0.30 / 1M$1.25 / 1M$2.00 / 1M
Output$2.50 / 1M$4.25 / 1M$10.00 / 1M
Cache Write (5m)$0.30 / 1M$1.25 / 1M$2.00 / 1M
Cache Write (1h)$0.30 / 1M$1.25 / 1M$2.00 / 1M
Cache Read$0.30 / 1M$1.25 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleMetaGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryGemini 3.5 Flash-Lite is Google's high-efficiency model with enhanced agentic capabilities, optimized for fast, cost-effective inference. It is designed to handle focused tasks with low latency while maintaining strong reasoning and execution quality. Well suited for subagents in complex multi-agent systems, Gemini 3.5 Flash-Lite excels at executing specialized tasks within larger workflows, making it ideal for scalable agent orchestration and high-throughput production environments.Muse 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.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.