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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 2GoogleRemove
  2. Gemini 3.8 FlashGoogleRemove
  3. GLM 5.3 FlashZ.AIRemove
gemini-embedding-2-preview vs gemini-3.8-flash vs glm-5.3-flash
AttributeGemini Embedding 2gemini-embedding-2-previewGemini 3.8 Flashgemini-3.8-flashGLM 5.3 Flashglm-5.3-flash
Pricing
Input$0.60 / 1M$0.75 / 1M$0.075 / 1M
Output$2.40 / 1M$3.75 / 1M$0.25 / 1M
Cache Write (5m)$0.60 / 1M$0.75 / 1M$0.075 / 1M
Cache Write (1h)$0.60 / 1M$0.75 / 1M$0.075 / 1M
Cache Read$0.60 / 1M$0.75 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context8.2K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleGoogleZ.AI
Categoryembeddingchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 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.Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.GLM-5.3-Flash is Z.AI's efficient native multimodal model, designed for coding and long-horizon agentic workflows. It combines strong multimodal capabilities with an architecture optimized for responsive, cost-efficient task execution. Built on a hybrid sparse and linear attention architecture, GLM-5.3-Flash maintains accurate long-context behavior while reducing computational overhead, making it well suited for coding agents, extended multi-step tasks, and scalable production workloads.