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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
claude-fable-5.2 vs gemini-embedding-2-preview
AttributeClaude Fable 5.2claude-fable-5.2Gemini Embedding 2gemini-embedding-2-preview
Pricing
Input$8.00 / 1M$0.60 / 1M
Output$40.00 / 1M$2.40 / 1M
Cache Write (5m)$10.00 / 1M$0.60 / 1M
Cache Write (1h)$16.00 / 1M$0.60 / 1M
Cache Read$0.20 / 1M$0.60 / 1M
Web Search$0 / 1M—
Context
Max context1M8.2K
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesNo
StreamingYesNo
Catalogue
ProviderAnthropicGoogle
Categorychatembedding
Charge typePay 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.