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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. Muse Spark 1.3MetaRemove
  4. Mistral SabaMistral AIRemove

4 is the maximum. Remove one to add another.

hy4-preview vs gpt-6-astra-pro vs muse-spark-1.3 vs mistral-saba
AttributeHy4 previewhy4-previewGPT-6 Astra Progpt-6-astra-proMuse Spark 1.3muse-spark-1.3Mistral Sabamistral-saba
Pricing
Input$0.834 / 1M$10.00 / 1M$1.25 / 1M$0.20 / 1M
Output$2.50 / 1M$50.00 / 1M$4.25 / 1M$0.60 / 1M
Cache Write (5m)$0.834 / 1M$10.00 / 1M$1.25 / 1M$0.20 / 1M
Cache Write (1h)$0.834 / 1M$10.00 / 1M$1.25 / 1M$0.20 / 1M
Cache Read$0.834 / 1M$10.00 / 1M$1.25 / 1M$0.20 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M32.8K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesNo
Function CallingYesYesYesNo
JSON ModeYesYesYesNo
StreamingYesYesYesNo
Catalogue
ProviderTencentOpenAIMetaMistral 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.Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.