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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 4 ArgonGoogleRemove
  2. mai-ds-r1:freeNot in the catalogueRemove
  3. Muse Spark 1.3MetaRemove
gemini-4-argon vs mai-ds-r1:free vs muse-spark-1.3
AttributeGemini 4 Argongemini-4-argonmai-ds-r1:freeNot in the catalogueMuse Spark 1.3muse-spark-1.3
Pricing
Input$2.00 / 1M—$1.25 / 1M
Output$10.00 / 1M—$4.25 / 1M
Cache Write (5m)$2.00 / 1M—$1.25 / 1M
Cache Write (1h)$2.00 / 1M—$1.25 / 1M
Cache Read$2.00 / 1M—$1.25 / 1M
Web Search$0 / 1M—$0 / 1M
Context
Max context1M—1M
Max outputN/A—N/A
Capabilities
VisionYes—Yes
Function CallingYes—Yes
JSON ModeYes—Yes
StreamingYes—Yes
Catalogue
ProviderGoogle—Meta
Categorychat—chat
Charge typePay As You Go—Pay As You Go
Released———
Description
SummaryGemini 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.