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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. Hy4 previewTencentRemove
  3. GLM 5.3Z.AIRemove
whisper-large-v3-turbo vs hy4-preview vs glm-5.3
AttributeWhisper Large V3 Turbowhisper-large-v3-turboHy4 previewhy4-previewGLM 5.3glm-5.3
Pricing
Input$3.33 / 1M$0.834 / 1M$1.40 / 1M
Output$0 / 1M$2.50 / 1M$4.40 / 1M
Cache Write (5m)Not applicable$0.834 / 1M$1.40 / 1M
Cache Write (1h)Not applicable$0.834 / 1M$1.40 / 1M
Cache ReadNot applicable$0.834 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesNo
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderOpenAITencentZ.AI
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.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.GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.