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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. Gemini 3.8 FlashGoogleRemove
  2. Muse Spark 1.3MetaRemove
  3. GPT-4o TranscribeOpenAIRemove
gemini-3.8-flash vs muse-spark-1.3 vs gpt-4o-transcribe
AttributeGemini 3.8 Flashgemini-3.8-flashMuse Spark 1.3muse-spark-1.3GPT-4o Transcribegpt-4o-transcribe
Pricing
Input$0.75 / 1M$1.25 / 1M$1.25 / 1M
Output$3.75 / 1M$4.25 / 1M$0 / 1M
Cache Write (5m)$0.75 / 1M$1.25 / 1MNot applicable
Cache Write (1h)$0.75 / 1M$1.25 / 1MNot applicable
Cache Read$0.75 / 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
ProviderGoogleMetaOpenAI
Categorychatchatvoice
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 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.GPT-4o Transcribe is OpenAI's high-quality speech-to-text model built on GPT-4o's audio capabilities. It delivers accurate transcription with strong language understanding, making it suitable for a wide range of audio processing tasks. Priced per token (input and output), it offers transparent, fine-grained billing, making it well suited for workflows that require scalable transcription, integration with LLM pipelines, and cost-aware processing.