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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. Kimi K3.1 ProMoonShot AIRemove
  2. Gemini Embedding 2GoogleRemove
kimi-k3.1-pro vs gemini-embedding-2-preview
AttributeKimi K3.1 Prokimi-k3.1-proGemini Embedding 2gemini-embedding-2-preview
Pricing
Input$2.00 / 1M$0.60 / 1M
Output$10.00 / 1M$2.40 / 1M
Cache Write (5m)$2.00 / 1M$0.60 / 1M
Cache Write (1h)$2.00 / 1M$0.60 / 1M
Cache Read$2.00 / 1M$0.60 / 1M
Web Search$0 / 1M—
Context
Max context1M8.2K
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesNo
StreamingYesNo
Catalogue
ProviderMoonShot AIGoogle
Categorychatembedding
Charge typePay As You GoPay As You Go
Released——
Description
SummaryKimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model, building on Kimi K3 with enhanced post-training for advanced reasoning and autonomous task execution. It features a 1M-token context window and native image and video understanding, enabling complex, long-context multimodal workflows. Optimized for long-horizon coding, 3D creation, financial research, and end-to-end agentic knowledge work, Kimi K3.1 Pro is well suited for autonomous software engineering, multimodal analysis, and complex professional workflows that require sustained reasoning and reliable execution.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.