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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. GPT-6 AstraOpenAIRemove
  2. Gemini Embedding 2GoogleRemove
  3. GLM 5.3 FlashZ.AIRemove
gpt-6-astra vs gemini-embedding-2-preview vs glm-5.3-flash
AttributeGPT-6 Astragpt-6-astraGemini Embedding 2gemini-embedding-2-previewGLM 5.3 Flashglm-5.3-flash
Pricing
Input$10.00 / 1M$0.60 / 1M$0.075 / 1M
Output$50.00 / 1M$2.40 / 1M$0.25 / 1M
Cache Write (5m)$10.00 / 1M$0.60 / 1M$0.075 / 1M
Cache Write (1h)$10.00 / 1M$0.60 / 1M$0.075 / 1M
Cache Read$10.00 / 1M$0.60 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context1M8.2K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderOpenAIGoogleZ.AI
Categorychatembeddingchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGPT-6 Astra is OpenAI's flagship model for demanding end-to-end professional work, designed for advanced analysis, software engineering, deep research, scientific tasks, and document creation. It is particularly strong in long-horizon agentic workflows, including tasks that require sustained reasoning, tool orchestration, and computer and browser use, making it well suited for complex autonomous workflows and production-grade knowledge work.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.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.