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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. Seedream 4.0ByteDanceRemove
  3. Muse Spark 1.3MetaRemove
hy4-preview vs doubao-seedream-4-0-250828 vs muse-spark-1.3
AttributeHy4 previewhy4-previewSeedream 4.0doubao-seedream-4-0-250828Muse Spark 1.3muse-spark-1.3
Pricing
Input$0.834 / 1M$1.25 / 1M
Output$2.50 / 1M$4.25 / 1M
Cache Write (5m)$0.834 / 1MNot applicable$1.25 / 1M
Cache Write (1h)$0.834 / 1MNot applicable$1.25 / 1M
Cache Read$0.834 / 1MNot applicable$1.25 / 1M
Web Search$0 / 1M$0 / 1M
Request$0.20 / request
BillingPay Per Request
Context
Max context1M128K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderTencentByteDanceMeta
Categorychatimagechat
Charge typePay As You GoPay Per RequestPay 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.Seedream 4.0 is ByteDance's advanced text-to-image generation model, designed to deliver high-quality, visually rich outputs with strong prompt alignment and improved aesthetic control. It enhances spatial composition, lighting realism, and fine detail rendering compared to earlier versions in the Seedream series. Optimized for creative production workflows, Seedream 4.0 supports diverse artistic styles and complex scene generation, making it well suited for marketing assets, concept art, design iteration, and professional visual content creation.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.