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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. Muse Spark 1.3MetaRemove
  2. DeepSeek V3.2 (Thinking)DeepSeekRemove
  3. GPT-6 Astra ProOpenAIRemove
muse-spark-1.3 vs deepseek-v3.2-thinking vs gpt-6-astra-pro
AttributeMuse Spark 1.3muse-spark-1.3DeepSeek V3.2 (Thinking)deepseek-v3.2-thinkingGPT-6 Astra Progpt-6-astra-pro
Pricing
Input$1.25 / 1M$0.19 / 1M$10.00 / 1M
Output$4.25 / 1M$0.275 / 1M$50.00 / 1M
Cache Write (5m)$1.25 / 1M$0.19 / 1M$10.00 / 1M
Cache Write (1h)$1.25 / 1M$0.19 / 1M$10.00 / 1M
Cache Read$1.25 / 1M$0.19 / 1M$10.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M163.8K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMetaDeepSeekOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryMuse 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.DeepSeek-V3.2 is an efficiency-focused large model that combines strong reasoning with reliable tool use. It introduces DeepSeek Sparse Attention to lower compute costs for long contexts while preserving quality, and uses large-scale reinforcement learning to reach GPT-5-class reasoning (including top IMO/IOI results). An agentic task-synthesis pipeline improves how it reasons with tools in interactive settings — and developers can toggle reasoning on or off as needed.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.