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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. Mistral SabaMistral AIRemove
  2. GPT-6 Astra ProOpenAIRemove
  3. Hy4 previewTencentRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

mistral-saba vs gpt-6-astra-pro vs hy4-preview vs muse-spark-1.3
AttributeMistral Sabamistral-sabaGPT-6 Astra Progpt-6-astra-proHy4 previewhy4-previewMuse Spark 1.3muse-spark-1.3
Pricing
Input$0.20 / 1M$10.00 / 1M$0.834 / 1M$1.25 / 1M
Output$0.60 / 1M$50.00 / 1M$2.50 / 1M$4.25 / 1M
Cache Write (5m)$0.20 / 1M$10.00 / 1M$0.834 / 1M$1.25 / 1M
Cache Write (1h)$0.20 / 1M$10.00 / 1M$0.834 / 1M$1.25 / 1M
Cache Read$0.20 / 1M$10.00 / 1M$0.834 / 1M$1.25 / 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 AIOpenAITencentMeta
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released
Description
SummaryGPT-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.Tencent 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.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.