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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
Mistral Embed 2312Mistral AI Remove
Muse Spark 1.3Meta Remove

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gemini-3.7-flash vs mistral-embed-2312 vs muse-spark-1.3
Attribute	Gemini 3.7 Flashgemini-3.7-flash	Mistral Embed 2312mistral-embed-2312	Muse Spark 1.3muse-spark-1.3
Pricing
Input	$0.375 / 1M	$0.125 / 1M	$1.25 / 1M
Output	$1.88 / 1M	$0 / 1M	$4.25 / 1M
Cache Write (5m)	$0.375 / 1M	$0.125 / 1M	$1.25 / 1M
Cache Write (1h)	$0.375 / 1M	$0.125 / 1M	$1.25 / 1M
Cache Read	$0.375 / 1M	$0.125 / 1M	$1.25 / 1M
Web Search	$0 / 1M	—	$0 / 1M
Context
Max context	1M	8.2K	1M
Max output	N/A	N/A	N/A
Capabilities
Vision	Yes	No	Yes
Function Calling	Yes	No	Yes
JSON Mode	Yes	No	Yes
Streaming	Yes	No	Yes
Catalogue
Provider	Google	Mistral AI	Meta
Category	chat	embedding	chat
Charge type	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.	Mistral Embed is Mistral AI's text embedding model, built for semantic search and RAG workflows. It generates 1024-dimensional vectors that capture meaningful relationships between pieces of text.	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.