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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. Gemini Embedding 001GoogleRemove
  3. GLM 5.3 FlashZ.AIRemove
hy4-preview vs gemini-embedding-001 vs glm-5.3-flash
AttributeHy4 previewhy4-previewGemini Embedding 001gemini-embedding-001GLM 5.3 Flashglm-5.3-flash
Pricing
Input$0.834 / 1M$0.075 / 1M$0.075 / 1M
Output$2.50 / 1M$0.30 / 1M$0.25 / 1M
Cache Write (5m)$0.834 / 1M$0.075 / 1M$0.075 / 1M
Cache Write (1h)$0.834 / 1M$0.075 / 1M$0.075 / 1M
Cache Read$0.834 / 1M$0.075 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context1M128K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesYesYes
Catalogue
ProviderTencentGoogleZ.AI
Categorychatembeddingchat
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.Gemini-Embedding-001 is Google's high-quality text embedding model designed for semantic understanding and retrieval tasks. It converts text into dense vector representations optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. The model emphasizes strong multilingual performance, high semantic accuracy, and efficient embedding generation, making it well suited for large-scale knowledge indexing and production retrieval pipelines.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.