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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. Hy4 previewTencentRemove
  2. Muse Spark OpenMetaRemove
  3. Qwen3.8 27BAlibabaRemove
hy4-preview vs muse-spark-open vs qwen3.8-27b
AttributeHy4 previewhy4-previewMuse Spark Openmuse-spark-openQwen3.8 27Bqwen3.8-27b
Pricing
Input$0.834 / 1M$1.25 / 1M$0.45 / 1M
Output$2.50 / 1M$4.25 / 1M$3.20 / 1M
Cache Write (5m)$0.834 / 1M$1.25 / 1M$0.45 / 1M
Cache Write (1h)$0.834 / 1M$1.25 / 1M$0.45 / 1M
Cache Read$0.834 / 1M$1.25 / 1M$0.45 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M262K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderTencentMetaAlibaba
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryTencent 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.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.