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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. GPT-6 Astra ProOpenAIRemove
  3. GLM 5.3Z.AIRemove
  4. Mistral Large 2407Mistral AIRemove

4 is the maximum. Remove one to add another.

hy4-preview vs gpt-6-astra-pro vs glm-5.3 vs mistral-large-2407
AttributeHy4 previewhy4-previewGPT-6 Astra Progpt-6-astra-proGLM 5.3glm-5.3Mistral Large 2407mistral-large-2407
Pricing
Input$0.834 / 1M$10.00 / 1M$1.40 / 1M$6.00 / 1M
Output$2.50 / 1M$50.00 / 1M$4.40 / 1M$18.00 / 1M
Cache Write (5m)$0.834 / 1M$10.00 / 1M$1.40 / 1M$6.00 / 1M
Cache Write (1h)$0.834 / 1M$10.00 / 1M$1.40 / 1M$6.00 / 1M
Cache Read$0.834 / 1M$10.00 / 1M$1.40 / 1M$6.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M131.1K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoNo
Function CallingYesYesYesNo
JSON ModeYesYesYesNo
StreamingYesYesYesNo
Catalogue
ProviderTencentOpenAIZ.AIMistral 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 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.