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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. Seedream 4.0ByteDanceRemove
  2. GPT-6 AstraOpenAIRemove
  3. Muse Spark 1.3MetaRemove
doubao-seedream-4-0-250828 vs gpt-6-astra vs muse-spark-1.3
AttributeSeedream 4.0doubao-seedream-4-0-250828GPT-6 Astragpt-6-astraMuse Spark 1.3muse-spark-1.3
Pricing
Request$0.20 / request
BillingPay Per Request
Cache Write (5m)Not applicable$10.00 / 1M$1.25 / 1M
Cache Write (1h)Not applicable$10.00 / 1M$1.25 / 1M
Cache ReadNot applicable$10.00 / 1M$1.25 / 1M
Input$10.00 / 1M$1.25 / 1M
Output$50.00 / 1M$4.25 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context128K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderByteDanceOpenAIMeta
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released
Description
SummarySeedream 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.GPT-6 Astra is OpenAI's flagship model for demanding end-to-end professional work, designed for advanced analysis, software engineering, deep research, scientific tasks, and document creation. It is particularly strong in long-horizon agentic workflows, including tasks that require sustained reasoning, tool orchestration, and computer and browser use, making it well suited for complex autonomous workflows and production-grade knowledge work.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.