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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. Claude Fable 5.1AnthropicRemove
  3. Qwen3.8 2.4T A95BAlibabaRemove
muse-spark-1.3 vs claude-fable-5.1 vs qwen3.8-2.4t-a95b
AttributeMuse Spark 1.3muse-spark-1.3Claude Fable 5.1claude-fable-5.1Qwen3.8 2.4T A95Bqwen3.8-2.4t-a95b
Pricing
Input$1.25 / 1M$10.00 / 1M$1.80 / 1M
Output$4.25 / 1M$50.00 / 1M$5.40 / 1M
Cache Write (5m)$1.25 / 1M$12.50 / 1M$1.80 / 1M
Cache Write (1h)$1.25 / 1M$20.00 / 1M$1.80 / 1M
Cache Read$1.25 / 1M$1.00 / 1M$1.80 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M262K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMetaAnthropicAlibaba
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.Claude 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.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.