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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. mai-ds-r1:freeNot in the catalogueRemove
  2. Gemini 4 ArgonGoogleRemove
  3. Hy4 previewTencentRemove
  4. Muse Spark OpenMetaRemove

4 is the maximum. Remove one to add another.

mai-ds-r1:free vs gemini-4-argon vs hy4-preview vs muse-spark-open
Attributemai-ds-r1:freeNot in the catalogueGemini 4 Argongemini-4-argonHy4 previewhy4-previewMuse Spark Openmuse-spark-open
Pricing
Input—$2.00 / 1M$0.834 / 1M$1.25 / 1M
Output—$10.00 / 1M$2.50 / 1M$4.25 / 1M
Cache Write (5m)—$2.00 / 1M$0.834 / 1M$1.25 / 1M
Cache Write (1h)—$2.00 / 1M$0.834 / 1M$1.25 / 1M
Cache Read—$2.00 / 1M$0.834 / 1M$1.25 / 1M
Web Search—$0 / 1M$0 / 1M$0 / 1M
Context
Max context—1M1M1M
Max output—N/AN/AN/A
Capabilities
Vision—YesYesYes
Function Calling—YesYesYes
JSON Mode—YesYesYes
Streaming—YesYesYes
Catalogue
Provider—GoogleTencentMeta
Category—chatchatchat
Charge type—Pay As You GoPay As You GoPay As You Go
Released————
Description
Summary—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.Tencent Hy4 Preview is a Mixture-of-Experts (MoE) model from Tencent, featuring 770B total parameters with 49B activated per token. It is designed for coding agents, complex tool-driven workflows, and professional productivity tasks that require strong planning and reliable execution. Optimized for context continuity and sustained multi-step work, Hy4 Preview is well suited for long-horizon coding, agentic automation, tool orchestration, and complex real-world workflows.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.