Llama Guard 3 8B

Meta chattool_use

API ID: meta-llama/llama-guard-3-8b

Input Price
$0.02
/1M tokens
Output Price
$0.06
/1M tokens

About Llama Guard 3 8B

Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM โ€“ it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated. Llama Guard 3 was aligned to safeguard against the MLCommons standardized hazards taxonomy and designed to support Llama 3.1 capabilities. Specifically, it provides content moderation in 8 languages, and was optimized to support safety and security for search and code interpreter tool calls.

๐Ÿ“Š
Price Ranking
#593 lowest price among 1240 Chat models

Model Specifications

Context Length
131k
Max Output
โ€”
Release Date
2025-02-12
Capabilities
chat tool_use
Input Modalities
text
Output Modalities
text

Best For

  • Conversations, content writing, general assistance

Consider Alternatives For

  • Image understanding (needs vision capability)

๐Ÿ’ฐ Real-World Cost Examples

Estimated monthly costs for common use cases

Personal AI Assistant
$0.03
/month
50 conversations/day, ~500 tokens each
Customer Service Bot
$0.84
/month
1000 tickets/day, ~800 tokens each

Meta Model Lineup

Compare all models from Meta to find the best fit

Model Input Output Context Capabilities
Llama Guard 3 8B Current Free Free 131k chat tool_use
Llama 3 70B (Base) Free Free 8k chat
Llama 3 70B (Base) Free Free 8k chat
LlamaGuard 2 8B Free Free 8k chat
Llama 3 8B (Base) Free Free 8k chat
Llama 3 8B (Base) Free Free 8k chat

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๐Ÿš€ Quick Start

Get started with Llama Guard 3 8B API

OpenAI-compatible SDK
from openai import OpenAI

client = OpenAI(
    base_url="https://api.provider.com/v1",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="meta-llama/llama-guard-3-8b",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)
print(response.choices[0].message.content)