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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. Claude Fable 5.1AnthropicRemove
  2. GPT-6 Luna ProOpenAIRemove
  3. Muse Spark 1.3MetaRemove
claude-fable-5.1 vs gpt-6-luna-pro vs muse-spark-1.3
AttributeClaude Fable 5.1claude-fable-5.1GPT-6 Luna Progpt-6-luna-proMuse Spark 1.3muse-spark-1.3
Pricing
Input$10.00 / 1M$0.10 / 1M$1.25 / 1M
Output$50.00 / 1M$0.50 / 1M$4.25 / 1M
Cache Write (5m)$12.50 / 1M$0.10 / 1M$1.25 / 1M
Cache Write (1h)$20.00 / 1M$0.10 / 1M$1.25 / 1M
Cache Read$0.25 / 1M$0.10 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1.1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderAnthropicOpenAIMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryClaude Fable 5.1 is an upgraded version of Fable 5, delivering broad improvements with particularly strong gains in agentic coding, long-running workflows, and professional knowledge work. It excels at large code refactors, front-end and visual code generation, financial analysis, and complex analytical tasks. Compared with Fable 5, it also produces more concise plans and summaries while maintaining strong performance across extended tasks, making it a natural upgrade for existing Fable workflows and a strong option alongside Opus 5 for reasoning-intensive applications.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.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.