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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. Nemotron Nano 9B V2 (Free)NVIDIARemove
  3. Muse Spark 1.3MetaRemove
claude-fable-5.1 vs nemotron-nano-9b-v2 vs muse-spark-1.3
AttributeClaude Fable 5.1claude-fable-5.1Nemotron Nano 9B V2 (Free)nemotron-nano-9b-v2Muse Spark 1.3muse-spark-1.3
Pricing
Input$10.00 / 1M$0 / 1M$1.25 / 1M
Output$50.00 / 1M$0 / 1M$4.25 / 1M
Cache Write (5m)$12.50 / 1M$1.25 / 1M
Cache Write (1h)$20.00 / 1M$1.25 / 1M
Cache Read$1.00 / 1M$0 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M
Cache Write$0 / 1M
Context
Max context1M131.1K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderAnthropicNVIDIAMeta
Categorychatchatchat
Charge typePay As You GoFreePay 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.NVIDIA Nemotron Nano 9B v2 is a 9B-parameter language model trained from scratch by NVIDIA, designed to handle both reasoning and non-reasoning tasks. It can generate an internal reasoning trace before producing a final answer, and this behavior is configurable via system prompts—allowing developers to enable or suppress visible reasoning as needed.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.