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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. Pixtral 12BMistral AIRemove
  2. Hy4 previewTencentRemove
  3. GPT-6 Astra ProOpenAIRemove
  4. GLM 5.3 FlashZ.AIRemove

4 is the maximum. Remove one to add another.

pixtral-12b vs hy4-preview vs gpt-6-astra-pro vs glm-5.3-flash
AttributePixtral 12Bpixtral-12bHy4 previewhy4-previewGPT-6 Astra Progpt-6-astra-proGLM 5.3 Flashglm-5.3-flash
Pricing
Input$0.10 / 1M$0.834 / 1M$10.00 / 1M$0.075 / 1M
Output$0.10 / 1M$2.50 / 1M$50.00 / 1M$0.25 / 1M
Cache Write (5m)$0.10 / 1M$0.834 / 1M$10.00 / 1M$0.075 / 1M
Cache Write (1h)$0.10 / 1M$0.834 / 1M$10.00 / 1M$0.075 / 1M
Cache Read$0.10 / 1M$0.834 / 1M$10.00 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context32.8K1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderMistral AITencentOpenAIZ.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.