LightweightOpen SourceMeta

Llama 3.2 1B

Name: Llama 3.2 1B
Availability: InStock
Author: Meta

Llama 3.2 1B is Meta's lightweight open-source text model with a 128K token context window, designed for efficient deployment and edge computing applications.

Context 128K

Tier Lightweight

Tools Supported

License Open Source

Input from

$0.027 / 1M tokens

across 3 providers

Compare Prices

API Pricing

Cheapest on OpenRouter — 57% below avg

Provider	Input / 1M	Output / 1M	Updated
OpenRouter	$0.027	$0.200	4/14/2026
Together AI	$0.060	$0.060	4/14/2026
Amazon AWS	$0.100	$0.100	4/14/2026

Prices updated daily. Last check: 4/14/2026

Model Details

General

Creator: Meta
Family: Llama
Tier: Lightweight
Context Window: 128K
Modalities: Text

Capabilities

Tool Calling: Yes
Open Source: Yes
Subtypes: Chat Completion

Strengths & Limitations

Open-source with publicly available model weights for local deployment
Compact 1B parameter size enables efficient inference and lower resource requirements
128K token context window provides substantial text processing capacity for its size
Tool calling support allows integration with external APIs and functions
Suitable for edge computing and mobile deployment scenarios
No API dependency required when running locally
Fine-tuning possible due to open-source availability

Limited to text-only input and output with no multimodal capabilities
Smaller parameter count results in lower capability compared to larger models in the Llama family
Performance on complex reasoning tasks limited relative to frontier models
May require fine-tuning for specialized domain applications

Key Features

•128K token context window

•Text-based chat completion

•Tool calling with function execution

•Open-source model weights

•1 billion parameter architecture

•Local deployment capability

•Streaming response support

•Fine-tuning compatibility

About Llama 3.2 1B

Llama 3.2 1B is Meta's compact text generation model from the Llama 3.2 series, positioned as the lightweight option in Meta's model family. With only 1 billion parameters, it represents Meta's effort to create efficient models suitable for resource-constrained environments while maintaining reasonable performance for text generation tasks. The model features a 128K token context window and supports text-only input and output with chat completion capabilities. It includes tool calling functionality, allowing it to interact with external APIs and functions. As an open-source model, the weights are publicly available for download and local deployment, enabling developers to run inference on their own infrastructure or fine-tune the model for specific applications. Llama 3.2 1B is primarily used for applications where computational efficiency and deployment flexibility are priorities over maximum capability. Its small size makes it suitable for edge computing, mobile applications, and scenarios requiring fast inference with lower computational overhead compared to larger frontier models.

Common Use Cases

Llama 3.2 1B is well-suited for applications requiring efficient text processing with moderate complexity, including chatbots for basic customer service, content summarization, simple question answering, and text classification tasks. Its lightweight nature makes it ideal for edge computing scenarios, mobile applications, and situations where running models locally is preferred over API calls. The model works well for prototyping, educational purposes, and applications where deployment speed and resource efficiency are more important than maximum capability. Organizations with data privacy requirements benefit from its ability to run entirely on-premises without external API dependencies.

Frequently Asked Questions

How much does Llama 3.2 1B cost per million tokens?

Llama 3.2 1B pricing varies by provider and deployment method. Since it's open-source, you can also run it locally without per-token costs. Check the pricing table above for current API rates across different providers.

What is Llama 3.2 1B best used for?

Llama 3.2 1B excels at efficient text processing tasks including basic chatbots, content summarization, simple question answering, and text classification. Its lightweight 1B parameter design makes it ideal for edge computing, mobile apps, and local deployment scenarios where resource efficiency is prioritized over maximum capability.

Can I run Llama 3.2 1B locally instead of using an API?

Yes, Llama 3.2 1B is open-source with publicly available model weights, allowing you to download and run it locally on your own hardware. Its compact 1B parameter size makes local deployment more feasible compared to larger models, though you'll need appropriate hardware and inference software to run it effectively.