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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. Muse Spark 1.3MetaRemove
  2. GLM 5.3 FlashZ.AIRemove
  3. GLM 5.3Z.AIRemove
  4. Gemini Embedding 001GoogleRemove

4 is the maximum. Remove one to add another.

muse-spark-1.3 vs glm-5.3-flash vs glm-5.3 vs gemini-embedding-001
AttributeMuse Spark 1.3muse-spark-1.3GLM 5.3 Flashglm-5.3-flashGLM 5.3glm-5.3Gemini Embedding 001gemini-embedding-001
Pricing
Input$1.25 / 1M$0.075 / 1M$1.40 / 1M$0.075 / 1M
Output$4.25 / 1M$0.25 / 1M$4.40 / 1M$0.30 / 1M
Cache Write (5m)$1.25 / 1M$0.075 / 1M$1.40 / 1M$0.075 / 1M
Cache Write (1h)$1.25 / 1M$0.075 / 1M$1.40 / 1M$0.075 / 1M
Cache Read$1.25 / 1M$0.075 / 1M$1.40 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M128K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoNo
Function CallingYesYesYesNo
JSON ModeYesYesYesNo
StreamingYesYesYesYes
Catalogue
ProviderMetaZ.AIZ.AIGoogle
Categorychatchatchatembedding
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released
Description
SummaryMuse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.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.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.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.