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Compare models

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. GPT-6 AstraOpenAIRemove
  3. GLM 5.3Z.AIRemove
gemini-embedding-2-preview vs gpt-6-astra vs glm-5.3
AttributeGemini Embedding 2gemini-embedding-2-previewGPT-6 Astragpt-6-astraGLM 5.3glm-5.3
Pricing
Input$0.60 / 1M$10.00 / 1M$1.40 / 1M
Output$2.40 / 1M$50.00 / 1M$4.40 / 1M
Cache Write (5m)$0.60 / 1M$10.00 / 1M$1.40 / 1M
Cache Write (1h)$0.60 / 1M$10.00 / 1M$1.40 / 1M
Cache Read$0.60 / 1M$10.00 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context8.2K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleOpenAIZ.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.GPT-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.GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.