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

4 is the maximum. Remove one to add another.

muse-spark-open vs hy4-preview vs gemini-4-argon vs mai-ds-r1:free
AttributeMuse Spark Openmuse-spark-openHy4 previewhy4-previewGemini 4 Argongemini-4-argonmai-ds-r1:freeNot in the catalogue
Pricing
Input$1.25 / 1M$0.834 / 1M$2.00 / 1M—
Output$4.25 / 1M$2.50 / 1M$10.00 / 1M—
Cache Write (5m)$1.25 / 1M$0.834 / 1M$2.00 / 1M—
Cache Write (1h)$1.25 / 1M$0.834 / 1M$2.00 / 1M—
Cache Read$1.25 / 1M$0.834 / 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
ProviderMetaTencentGoogle—
Categorychatchatchat—
Charge typePay As You GoPay As You GoPay As You Go—
Released————
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.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.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.—