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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 4.5ByteDanceRemove
  2. GPT-6 AstraOpenAIRemove
  3. Muse Spark 1.3MetaRemove
doubao-seedream-4-5-251128 vs gpt-6-astra vs muse-spark-1.3
AttributeSeedream 4.5doubao-seedream-4-5-251128GPT-6 Astragpt-6-astraMuse Spark 1.3muse-spark-1.3
Pricing
Request$0.375 / 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 4.5 is ByteDance's advanced AI image generation and editing model, representing a major evolution of the Seedream family. It delivers professional-grade visual quality with rich detail, improved spatial understanding, and cinematic rendering effects. Seedream 4.5 excels at understanding nuanced natural language prompts and generating consistent, high-fidelity outputs with enhanced lighting, depth, and texture. It supports complex workflows such as multi-image composition, fine typography and text rendering, and image-to-image editing with enhanced prompt interpretation.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.