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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 V3OpenAIRemove
  2. Hy4 previewTencentRemove
  3. Muse Spark 1.3MetaRemove
whisper-large-v3 vs hy4-preview vs muse-spark-1.3
AttributeWhisper Large V3whisper-large-v3Hy4 previewhy4-previewMuse Spark 1.3muse-spark-1.3
Pricing
Input$9.25 / 1M$0.834 / 1M$1.25 / 1M
Output$0 / 1M$2.50 / 1M$4.25 / 1M
Cache Write (5m)Not applicable$0.834 / 1M$1.25 / 1M
Cache Write (1h)Not applicable$0.834 / 1M$1.25 / 1M
Cache ReadNot applicable$0.834 / 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 ModeYesYesYes
StreamingNoYesYes
Catalogue
ProviderOpenAITencentMeta
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryWhisper Large V3 is OpenAI's advanced open-source automatic speech recognition (ASR) model, supporting both audio transcription and translation across 99+ languages. It accepts common audio formats including mp3, mp4, wav, webm, flac, and ogg, and delivers strong performance in noisy, real-world conditions. With 1.55B parameters and a low 10.3% word error rate, it provides accurate, multilingual transcription with support for word- and segment-level timestamps, making it well suited for high-quality, noise-robust speech processing applications.Tencent Hy4 Preview is a Mixture-of-Experts (MoE) model from Tencent, featuring 770B total parameters with 49B activated per token. It is designed for coding agents, complex tool-driven workflows, and professional productivity tasks that require strong planning and reliable execution. Optimized for context continuity and sustained multi-step work, Hy4 Preview is well suited for long-horizon coding, agentic automation, tool orchestration, and complex real-world workflows.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.