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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 001GoogleRemove
  2. Hy4 previewTencentRemove
  3. Muse Spark 1.3MetaRemove
  4. Gemini 3.7 FlashGoogleRemove

4 is the maximum. Remove one to add another.

gemini-embedding-001 vs hy4-preview vs muse-spark-1.3 vs gemini-3.7-flash
AttributeGemini Embedding 001gemini-embedding-001Hy4 previewhy4-previewMuse Spark 1.3muse-spark-1.3Gemini 3.7 Flashgemini-3.7-flash
Pricing
Input$0.075 / 1M$0.834 / 1M$1.25 / 1M$0.375 / 1M
Output$0.30 / 1M$2.50 / 1M$4.25 / 1M$1.88 / 1M
Cache Write (5m)$0.075 / 1M$0.834 / 1M$1.25 / 1M$0.375 / 1M
Cache Write (1h)$0.075 / 1M$0.834 / 1M$1.25 / 1M$0.375 / 1M
Cache Read$0.075 / 1M$0.834 / 1M$1.25 / 1M$0.375 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context128K1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderGoogleTencentMetaGoogle
Categoryembeddingchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini-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.Tencent Hy4 Preview is a Mixture-of-Experts (MoE) model from Tencent, featuring 770B total parameters with 49B activated per token. It is designed for coding agents, complex tool-driven workflows, and professional productivity tasks that require strong planning and reliable execution. Optimized for context continuity and sustained multi-step work, Hy4 Preview is well suited for long-horizon coding, agentic automation, tool orchestration, and complex real-world workflows.Muse 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.Gemini 3.7 Flash is Google's fast multimodal model designed for agentic workflows, coding, and complex multi-step reasoning. It combines responsive inference with reliable problem-solving capabilities, making it well suited for interactive and production-scale applications. Optimized for speed and dependable multi-step execution, Gemini 3.7 Flash is a strong choice for coding assistants, autonomous agents, and high-throughput workflows that require both low latency and capable reasoning.