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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. GLM 5.3Z.AIRemove
  2. Gemini Embedding 001GoogleRemove
  3. Claude Fable 5.1AnthropicRemove
glm-5.3 vs gemini-embedding-001 vs claude-fable-5.1
AttributeGLM 5.3glm-5.3Gemini Embedding 001gemini-embedding-001Claude Fable 5.1claude-fable-5.1
Pricing
Input$1.40 / 1M$0.075 / 1M$10.00 / 1M
Output$4.40 / 1M$0.30 / 1M$50.00 / 1M
Cache Write (5m)$1.40 / 1M$0.075 / 1M$12.50 / 1M
Cache Write (1h)$1.40 / 1M$0.075 / 1M$20.00 / 1M
Cache Read$1.40 / 1M$0.075 / 1M$1.00 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context1M128K1M
Max outputN/AN/AN/A
Capabilities
VisionNoNoYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesYesYes
Catalogue
ProviderZ.AIGoogleAnthropic
Categorychatembeddingchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGLM-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.Gemini-Embedding-001 is Google's high-quality text embedding model designed for semantic understanding and retrieval tasks. It converts text into dense vector representations optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. The model emphasizes strong multilingual performance, high semantic accuracy, and efficient embedding generation, making it well suited for large-scale knowledge indexing and production retrieval pipelines.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.