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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. Qwen3.8 2.4T A95BAlibabaRemove
  3. GPT-6 Luna ProOpenAIRemove
muse-spark-1.3 vs qwen3.8-2.4t-a95b vs gpt-6-luna-pro
AttributeMuse Spark 1.3muse-spark-1.3Qwen3.8 2.4T A95Bqwen3.8-2.4t-a95bGPT-6 Luna Progpt-6-luna-pro
Pricing
Input$1.25 / 1M$1.80 / 1M$0.10 / 1M
Output$4.25 / 1M$5.40 / 1M$0.50 / 1M
Cache Write (5m)$1.25 / 1M$1.80 / 1M$0.10 / 1M
Cache Write (1h)$1.25 / 1M$1.80 / 1M$0.10 / 1M
Cache Read$1.25 / 1M$1.80 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M262K1.1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMetaAlibabaOpenAI
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.Qwen3.8 2.4T A95B is Qwen's open-weight sparse Mixture-of-Experts (MoE) model and the open-weight counterpart to Qwen3.8 Max. It features 2.4T total parameters with 95B activated per token, combining frontier-scale capacity with efficient sparse inference. Designed for coding, research, complex reasoning, and agentic workflows, the model is well suited for demanding long-horizon tasks and advanced autonomous systems while providing the flexibility and customization benefits of open weights.GPT-6 Luna Pro uses the same underlying model as GPT-6 Luna, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. It combines Luna's speed and cost efficiency with deeper reasoning and more reliable multi-step execution, making it well suited for advanced coding, computer use, and agentic workflows where higher solution quality is needed without moving to a larger GPT-6 tier.