LightweightOpen SourceMeta

Llama 3.2 3B

Llama 3.2 3B is Meta's lightweight open-source model optimized for efficient deployment with tool calling support and 128K token context window.

Context 128K
Tier Lightweight
Tools Supported
License Open Source
Input from
$0.051 / 1M tokens
across 2 providers

API Pricing

Cheapest on OpenRouter 49% below avg
ProviderInput / 1MOutput / 1MUpdated
$0.051$0.3404/14/2026
$0.150$0.1504/14/2026

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 full model weights available for custom deployment
  • 128K token context window for processing long documents
  • Tool calling support enables API integrations and function execution
  • Compact 3B parameter size allows efficient inference on modest hardware
  • Part of Meta's actively maintained Llama ecosystem
  • Suitable for edge deployment and resource-constrained environments
  • Lower computational requirements reduce operational costs
  • Limited reasoning capabilities compared to larger models in the family
  • Text-only modality with no image or multimodal support
  • Smaller parameter count may impact performance on complex tasks
  • May require fine-tuning for specialized domain applications

Key Features

128K token context window
Tool calling with function execution
Chat completion interface
Open-source model weights
Streaming response support
Custom deployment capabilities
Fine-tuning compatibility
Efficient inference architecture

About Llama 3.2 3B

Llama 3.2 3B is Meta's lightweight text model in the Llama family, positioned as an efficient alternative to larger models for applications requiring faster inference and lower resource consumption. As an open-source model, it provides developers with full access to model weights for custom deployment and fine-tuning. The model features a 128K token context window and supports tool calling capabilities, enabling it to interact with external APIs and functions. Despite its compact 3 billion parameter size, it maintains the architectural improvements of the Llama 3.2 generation while prioritizing speed and efficiency over raw capability compared to its larger siblings. Llama 3.2 3B is commonly used for applications where response latency and computational costs are primary concerns, such as real-time chat applications, edge deployment scenarios, and high-volume text processing tasks that don't require the most advanced reasoning capabilities of frontier models.

Common Use Cases

Llama 3.2 3B is well-suited for applications prioritizing speed and efficiency over maximum capability, including real-time chat systems, content moderation at scale, document summarization, and API-powered assistants with tool calling requirements. Its lightweight nature makes it ideal for edge computing scenarios, mobile applications, and high-throughput processing where response latency matters more than complex reasoning. The open-source availability also makes it valuable for organizations requiring on-premises deployment or custom fine-tuning for specific domains.

Frequently Asked Questions

How much does Llama 3.2 3B cost per million tokens?

Llama 3.2 3B pricing varies significantly by provider and deployment method. Since it's open-source, you can also self-host to avoid per-token charges entirely. Check the pricing table above for current rates across all providers.

What is Llama 3.2 3B best used for?

Llama 3.2 3B excels at applications requiring fast, efficient text processing with tool calling capabilities. It's ideal for real-time chat, content moderation, document summarization, and scenarios where response speed and cost efficiency matter more than advanced reasoning capabilities.

How does Llama 3.2 3B compare to larger Llama models?

Llama 3.2 3B trades reasoning capability for speed and efficiency compared to larger models in the family. While it has the same 128K context window and tool calling support, its 3B parameter size means faster inference and lower costs but reduced performance on complex reasoning tasks.