Skip to content

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-4o Mini TranscribeOpenAIRemove
  2. Muse Spark 1.3MetaRemove
  3. GPT-6 Astra ProOpenAIRemove
gpt-4o-mini-transcribe vs muse-spark-1.3 vs gpt-6-astra-pro
AttributeGPT-4o Mini Transcribegpt-4o-mini-transcribeMuse Spark 1.3muse-spark-1.3GPT-6 Astra Progpt-6-astra-pro
Pricing
Input$0.625 / 1M$1.25 / 1M$10.00 / 1M
Output$0.625 / 1M$4.25 / 1M$50.00 / 1M
Cache Write (5m)Not applicable$1.25 / 1M$10.00 / 1M
Cache Write (1h)Not applicable$1.25 / 1M$10.00 / 1M
Cache ReadNot applicable$1.25 / 1M$10.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context128K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeYesYesYes
StreamingNoYesYes
Catalogue
ProviderOpenAIMetaOpenAI
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGPT-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.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-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.