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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 3.7 FlashGoogleRemove
  2. Claude Fable 5.1AnthropicRemove
  3. Gemini Embedding 2GoogleRemove
gemini-3.7-flash vs claude-fable-5.1 vs gemini-embedding-2-preview
AttributeGemini 3.7 Flashgemini-3.7-flashClaude Fable 5.1claude-fable-5.1Gemini Embedding 2gemini-embedding-2-preview
Pricing
Input$0.375 / 1M$10.00 / 1M$0.60 / 1M
Output$1.88 / 1M$50.00 / 1M$2.40 / 1M
Cache Write (5m)$0.375 / 1M$12.50 / 1M$0.60 / 1M
Cache Write (1h)$0.375 / 1M$20.00 / 1M$0.60 / 1M
Cache Read$0.375 / 1M$1.00 / 1M$0.60 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context1M1M8.2K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesNo
StreamingYesYesNo
Catalogue
ProviderGoogleAnthropicGoogle
Categorychatchatembedding
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 3.7 Flash is Google's fast multimodal model designed for agentic workflows, coding, and complex multi-step reasoning. It combines responsive inference with reliable problem-solving capabilities, making it well suited for interactive and production-scale applications. Optimized for speed and dependable multi-step execution, Gemini 3.7 Flash is a strong choice for coding assistants, autonomous agents, and high-throughput workflows that require both low latency and capable reasoning.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.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.