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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. GPT-5.6 TerraOpenAIRemove
  2. Muse Spark 1.3MetaRemove
  3. GPT-6 Astra ProOpenAIRemove
gpt-5.6-terra vs muse-spark-1.3 vs gpt-6-astra-pro
AttributeGPT-5.6 Terragpt-5.6-terraMuse Spark 1.3muse-spark-1.3GPT-6 Astra Progpt-6-astra-pro
Pricing
Input$2.00 / 1M$1.25 / 1M$10.00 / 1M
Output$12.00 / 1M$4.25 / 1M$50.00 / 1M
Cache Write (5m)$2.00 / 1M$1.25 / 1M$10.00 / 1M
Cache Write (1h)$2.00 / 1M$1.25 / 1M$10.00 / 1M
Cache Read$2.00 / 1M$1.25 / 1M$10.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIMetaOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGPT-5.6 Terra is the balanced model in OpenAI's GPT-5.6 series, positioned between the flagship Sol tier and the cost-efficient Luna tier. It is designed for everyday coding, reasoning, and agentic workflows, delivering strong performance while balancing capability and cost. Offering near-flagship quality at approximately half the cost of Sol, GPT-5.6 Terra is well suited for production applications that require reliable reasoning, software development, and scalable agent execution.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.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.