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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. Hy4 previewTencentRemove
  2. GLM 5.3 FlashZ.AIRemove
  3. GPT-4o TranscribeOpenAIRemove
hy4-preview vs glm-5.3-flash vs gpt-4o-transcribe
AttributeHy4 previewhy4-previewGLM 5.3 Flashglm-5.3-flashGPT-4o Transcribegpt-4o-transcribe
Pricing
Input$0.834 / 1M$0.075 / 1M$1.25 / 1M
Output$2.50 / 1M$0.25 / 1M$0 / 1M
Cache Write (5m)$0.834 / 1M$0.075 / 1MNot applicable
Cache Write (1h)$0.834 / 1M$0.075 / 1MNot applicable
Cache Read$0.834 / 1M$0.075 / 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
ProviderTencentZ.AIOpenAI
Categorychatchatvoice
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryTencent 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-Flash is Z.AI's efficient native multimodal model, designed for coding and long-horizon agentic workflows. It combines strong multimodal capabilities with an architecture optimized for responsive, cost-efficient task execution. Built on a hybrid sparse and linear attention architecture, GLM-5.3-Flash maintains accurate long-context behavior while reducing computational overhead, making it well suited for coding agents, extended multi-step tasks, and scalable production workloads.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.