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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. GPT-6 Astra ProOpenAIRemove
  2. Muse Spark 1.3MetaRemove
  3. GPT-4o Mini TranscribeOpenAIRemove
gpt-6-astra-pro vs muse-spark-1.3 vs gpt-4o-mini-transcribe
AttributeGPT-6 Astra Progpt-6-astra-proMuse Spark 1.3muse-spark-1.3GPT-4o Mini Transcribegpt-4o-mini-transcribe
Pricing
Input$10.00 / 1M$1.25 / 1M$0.625 / 1M
Output$50.00 / 1M$4.25 / 1M$0.625 / 1M
Cache Write (5m)$10.00 / 1M$1.25 / 1MNot applicable
Cache Write (1h)$10.00 / 1M$1.25 / 1MNot applicable
Cache Read$10.00 / 1M$1.25 / 1MNot applicable
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M128K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesNo
JSON ModeYesYesYes
StreamingYesYesNo
Catalogue
ProviderOpenAIMetaOpenAI
Categorychatchatvoice
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost.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.GPT-4o Mini Transcribe is a smaller, cost-efficient speech-to-text model built on GPT-4o Mini's audio capabilities. It is designed for high-volume transcription workloads, delivering reliable performance with lower cost and latency. Priced per token (input and output), it provides transparent, fine-grained billing, making it well suited for scalable transcription pipelines, real-time applications, and cost-sensitive deployments.