meta-llama/llama-guard-4-12b
Release date: 2025-04-30
163,840 context · $0.18/M input tokens · $0.18/M output tokens
Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM...
Pricing
Pay-per-use
No upfront costs, pay only for what you use
Try the model
API Usage
Use the following code examples to integrate with our API:
import OpenAI from 'openai';
if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
apiKey: process.env.WAVESPEED_API_KEY,
baseURL: 'https://llm.wavespeed.ai/v1',
timeout: 120_000,
maxRetries: 0,
});
try {
const response = await client.chat.completions.create({
model: 'meta-llama/llama-guard-4-12b',
messages: [{ role: 'user', content: 'Hello!' }],
});
const body = response as typeof response & { error?: { message?: string } };
if (body.error) throw new Error(body.error.message || 'LLM generation failed');
console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
console.error('LLM request failed:', error);
process.exitCode = 1;
}import OpenAI from 'openai';
if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
apiKey: process.env.WAVESPEED_API_KEY,
baseURL: 'https://llm.wavespeed.ai/v1',
timeout: 120_000,
maxRetries: 0,
});
try {
const response = await client.chat.completions.create({
model: 'meta-llama/llama-guard-4-12b',
messages: [{ role: 'user', content: 'Hello!' }],
});
const body = response as typeof response & { error?: { message?: string } };
if (body.error) throw new Error(body.error.message || 'LLM generation failed');
console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
console.error('LLM request failed:', error);
process.exitCode = 1;
}Model Introduction
Meta: Llama Guard 4 12B
Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM...
This model is available through the WaveSpeed AI OpenAI-compatible API for chat completions and compatible application workflows.
Why It Looks Great
- text+image->text architecture for Image, Text to Text workloads
- 163840 context window for long prompts, document analysis, and multi-turn workflows
- Vision input support for image understanding and multimodal tasks
Key Features
- Context Window: 163840 tokens
- Max Input: 147456 tokens
- Max Output: 16384 tokens
- Vision: Supported
- Function Calling: Not listed
- Structured Outputs: Not listed
- Image Generation: Not listed
- Audio Input: Not listed
- Supported Parameters: frequency_penalty, logit_bias, max_tokens, min_p, presence_penalty, repetition_penalty, seed, stop, temperature, top_k, top_p
Specifications
| Specification | Value |
|---|---|
| Provider | meta |
| Model Type | Chat Completions model |
| Architecture | text+image->text |
| Context Window | 163840 tokens |
| Max Input | 147456 tokens |
| Max Output | 16384 tokens |
| Input | Image, Text |
| Output | Text |
| Vision | Supported |
| Function Calling | Not listed |
| Structured Outputs | Not listed |
| Release Date | April 30, 2025 |
How to Use
- Write your prompt - describe the task, provide context, and specify the desired output format.
- Submit - the model processes your request and returns the response.
API Integration
Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: meta-llama/llama-guard-4-12b
API Usage
Python SDK
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://llm.wavespeed.ai/v1"
)
response = client.chat.completions.create(
model="meta-llama/llama-guard-4-12b",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)cURL
curl https://llm.wavespeed.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "meta-llama/llama-guard-4-12b",
"messages": [{"role": "user", "content": "Hello!"}]
}'Notes
- Model: meta-llama/llama-guard-4-12b
- Provider: meta
Info
Supported Functionality
API Access Guide
Llama Guard 4 12b API
meta-llama/llama-guard-4-12b
Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM...
Input
$0.18 /M
Output
$0.18 /M
Context
164K
Max Output
16K
Vision
Supported
Try Llama Guard 4 12b on WaveSpeedAI
Access Llama Guard 4 12b through our unified API — OpenAI-compatible, no cold starts, transparent pricing.
Frequently Asked Questions about Llama Guard 4 12b
How much does Llama Guard 4 12b cost via the API?+
Pricing on WaveSpeedAI: $0.18 per million input tokens and $0.18 per million output tokens. Prompt caching and batch processing are billed separately and reduce effective cost on long, repetitive workloads.
What is the context window of Llama Guard 4 12b?+
Llama Guard 4 12b supports up to 164K tokens of context with up to 16K tokens of output per request.
Is Llama Guard 4 12b OpenAI-compatible?+
WaveSpeedAI exposes Llama Guard 4 12b at https://llm.wavespeed.ai/v1 through the OpenAI-compatible Chat Completions interface. Most OpenAI SDK clients work by changing the base URL and API key; optional fields depend on the selected model.
How do I get started with Llama Guard 4 12b?+
Sign in to WaveSpeedAI, create an API key in Access Keys, then send a request to https://llm.wavespeed.ai/v1/chat/completions with the model id shown above. Check the current model catalog for availability, capabilities, and pricing.