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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. Whisper Large V3 TurboOpenAIRemove
  2. Gemini 3.8 FlashGoogleRemove
  3. Muse Spark 1.3MetaRemove
whisper-large-v3-turbo vs gemini-3.8-flash vs muse-spark-1.3
AttributeWhisper Large V3 Turbowhisper-large-v3-turboGemini 3.8 Flashgemini-3.8-flashMuse Spark 1.3muse-spark-1.3
Pricing
Input$3.33 / 1M$0.75 / 1M$1.25 / 1M
Output$0 / 1M$3.75 / 1M$4.25 / 1M
Cache Write (5m)Not applicable$0.75 / 1M$1.25 / 1M
Cache Write (1h)Not applicable$0.75 / 1M$1.25 / 1M
Cache ReadNot applicable$0.75 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderOpenAIGoogleMeta
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryWhisper Large V3 Turbo is an optimized version of OpenAI's Whisper Large V3 speech recognition model, designed for high-speed and cost-efficient transcription. It supports 99+ languages and accepts common audio formats including mp3, mp4, wav, webm, flac, and ogg. With a ~12% word error rate and real-time speed factors up to 216×, it delivers fast, scalable performance for latency-sensitive and high-throughput transcription workloads, making it ideal for real-time and large-scale speech processing applications.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.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.