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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 5.2AnthropicRemove
  2. Gemini Embedding 2GoogleRemove
  3. Muse Spark OpenMetaRemove
claude-fable-5.2 vs gemini-embedding-2-preview vs muse-spark-open
AttributeClaude Fable 5.2claude-fable-5.2Gemini Embedding 2gemini-embedding-2-previewMuse Spark Openmuse-spark-open
Pricing
Input$8.00 / 1M$0.60 / 1M$1.25 / 1M
Output$40.00 / 1M$2.40 / 1M$4.25 / 1M
Cache Write (5m)$10.00 / 1M$0.60 / 1M$1.25 / 1M
Cache Write (1h)$16.00 / 1M$0.60 / 1M$1.25 / 1M
Cache Read$0.20 / 1M$0.60 / 1M$1.25 / 1M
Web Search$0 / 1M—$0 / 1M
Context
Max context1M8.2K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderAnthropicGoogleMeta
Categorychatembeddingchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryClaude Fable 5.2 is Anthropic's most capable model for advanced technical work and open-ended problem solving, delivering significant improvements in coding, reasoning, and vision over Fable 5.1. It excels at exceptionally complex tasks, including large-scale software migrations, electronic system design, and advanced scientific and mathematical research. With up to 30% faster output generation, clearer communication, and always-on adaptive extended thinking, Fable 5.2 supports configurable reasoning effort from low to max (default high), with thinking preserved across turns. Designed for frontier engineering, scientific discovery, and ambitious long-horizon agentic workflows, it complements Opus 5.5 as a specialized option for the most demanding technical challenges.Gemini Embedding 2 is Google's advanced text embedding model designed for high-accuracy semantic representation across large-scale retrieval and understanding tasks. It converts text into dense vector embeddings optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. Built for production use, it offers strong multilingual support, improved semantic similarity accuracy, and efficient embedding generation, making it well suited for large knowledge indexing pipelines and enterprise-scale retrieval applications.Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.