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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.

Llama 3.2 1b InstructMeta Remove
Gemini 3.7 FlashGoogle Remove
Muse Spark 1.3Meta Remove

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llama-3.2-1b-instruct vs gemini-3.7-flash vs muse-spark-1.3
Attribute	Llama 3.2 1b Instructllama-3.2-1b-instruct	Gemini 3.7 Flashgemini-3.7-flash	Muse Spark 1.3muse-spark-1.3
Pricing
Input	$0.0025 / 1M	$0.375 / 1M	$1.25 / 1M
Output	$0.005 / 1M	$1.88 / 1M	$4.25 / 1M
Cache Write (5m)	$0.0025 / 1M	$0.375 / 1M	$1.25 / 1M
Cache Write (1h)	$0.0025 / 1M	$0.375 / 1M	$1.25 / 1M
Cache Read	$0.0025 / 1M	$0.375 / 1M	$1.25 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	N/A	1M	1M
Max output	N/A	N/A	N/A
Capabilities
Vision	No	Yes	Yes
Function Calling	Yes	Yes	Yes
JSON Mode	Yes	Yes	Yes
Streaming	Yes	Yes	Yes
Catalogue
Provider	Meta	Google	Meta
Category	chat	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	—	—
Description
Summary	Llama 3.2 1B is a lightweight 1-billion-parameter model built for efficient NLP tasks like summarization, conversation, and multilingual analysis. It runs well in low-resource environments, supports eight core languages (and can be fine-tuned for more), making it a good fit for developers who need capable, multilingual AI without heavy compute costs.	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.	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.