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.

Gemini 3.7 FlashGoogle Remove
Gemini Embedding 001Google Remove
Hy4 previewTencent Remove
Muse Spark 1.3Meta Remove

4 is the maximum. Remove one to add another.

gemini-3.7-flash vs gemini-embedding-001 vs hy4-preview vs muse-spark-1.3
Attribute	Gemini 3.7 Flashgemini-3.7-flash	Gemini Embedding 001gemini-embedding-001	Hy4 previewhy4-preview	Muse Spark 1.3muse-spark-1.3
Pricing
Input	$0.375 / 1M	$0.075 / 1M	$0.834 / 1M	$1.25 / 1M
Output	$1.88 / 1M	$0.30 / 1M	$2.50 / 1M	$4.25 / 1M
Cache Write (5m)	$0.375 / 1M	$0.075 / 1M	$0.834 / 1M	$1.25 / 1M
Cache Write (1h)	$0.375 / 1M	$0.075 / 1M	$0.834 / 1M	$1.25 / 1M
Cache Read	$0.375 / 1M	$0.075 / 1M	$0.834 / 1M	$1.25 / 1M
Web Search	$0 / 1M	—	$0 / 1M	$0 / 1M
Context
Max context	1M	128K	1M	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	No	Yes	Yes
Function Calling	Yes	No	Yes	Yes
JSON Mode	Yes	No	Yes	Yes
Streaming	Yes	Yes	Yes	Yes
Catalogue
Provider	Google	Google	Tencent	Meta
Category	chat	embedding	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	—	—	—
Description
Summary	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.	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.	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.