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Compare models

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. Qwen PlusAlibabaRemove
  3. GPT-6 Astra ProOpenAIRemove
  4. GLM 5.3 FlashZ.AIRemove

4 is the maximum. Remove one to add another.

hy4-preview vs qwen-plus vs gpt-6-astra-pro vs glm-5.3-flash
AttributeHy4 previewhy4-previewQwen Plusqwen-plusGPT-6 Astra Progpt-6-astra-proGLM 5.3 Flashglm-5.3-flash
Pricing
Input$0.834 / 1M$0.40 / 1M$10.00 / 1M$0.075 / 1M
Output$2.50 / 1M$1.20 / 1M$50.00 / 1M$0.25 / 1M
Cache Write (5m)$0.834 / 1M$0.40 / 1M$10.00 / 1M$0.075 / 1M
Cache Write (1h)$0.834 / 1M$0.40 / 1M$10.00 / 1M$0.075 / 1M
Cache Read$0.834 / 1M$0.40 / 1M$10.00 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M131.1K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderTencentAlibabaOpenAIZ.AI
Categorychatchatchatchat
Charge typePay As You GoPay 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.GPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost.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.