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. Gemini 3.8 FlashGoogleRemove
gpt-4o-mini-transcribe vs muse-spark-1.3 vs gemini-3.8-flash
AttributeGPT-4o Mini Transcribegpt-4o-mini-transcribeMuse Spark 1.3muse-spark-1.3Gemini 3.8 Flashgemini-3.8-flash
Pricing
Input$0.625 / 1M$1.25 / 1M$0.75 / 1M
Output$0.625 / 1M$4.25 / 1M$3.75 / 1M
Cache Write (5m)Not applicable$1.25 / 1M$0.75 / 1M
Cache Write (1h)Not applicable$1.25 / 1M$0.75 / 1M
Cache ReadNot applicable$1.25 / 1M$0.75 / 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
ProviderOpenAIMetaGoogle
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.Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.