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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. Gemini Embedding 001GoogleRemove
  2. Claude Fable 5.1AnthropicRemove
  3. Muse Spark 1.3MetaRemove
gemini-embedding-001 vs claude-fable-5.1 vs muse-spark-1.3
AttributeGemini Embedding 001gemini-embedding-001Claude Fable 5.1claude-fable-5.1Muse Spark 1.3muse-spark-1.3
Pricing
Input$0.075 / 1M$10.00 / 1M$1.25 / 1M
Output$0.30 / 1M$50.00 / 1M$4.25 / 1M
Cache Write (5m)$0.075 / 1M$12.50 / 1M$1.25 / 1M
Cache Write (1h)$0.075 / 1M$20.00 / 1M$1.25 / 1M
Cache Read$0.075 / 1M$1.00 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context128K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleAnthropicMeta
Categoryembeddingchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini-Embedding-001 is Google's high-quality text embedding model designed for semantic understanding and retrieval tasks. It converts text into dense vector representations optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. The model emphasizes strong multilingual performance, high semantic accuracy, and efficient embedding generation, making it well suited for large-scale knowledge indexing and production retrieval pipelines.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.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.