Meta: Llama Guard 4 12B✓ Catalog verified
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 inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM—generating 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 4 was aligned to safeguard against the standardized MLCommons hazards taxonomy and designed to support multimodal Llama 4 capabilities. Specifically, it combines features from previous Llama Guard models, providing content moderation for English and multiple supported languages, along with enhanced capabilities to handle mixed text-and-image prompts, including multiple images. Additionally, Llama Guard 4 is integrated into the Llama Moderations API, extending robust safety classification to text and images.
Overview
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 inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM—generating 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 4 was aligned to safeguard against the standardized MLCommons hazards taxonomy and designed to support multimodal Llama 4 capabilities. Specifically, it combines features from previous Llama Guard models, providing content moderation for English and multiple supported languages, along with enhanced capabilities to handle mixed text-and-image prompts, including multiple images. Additionally, Llama Guard 4 is integrated into the Llama Moderations API, extending robust safety classification to text and images.
Access: available through the official Meta API. Context window: 164K.
Specifications
| API identifier | meta-llama/llama-guard-4-12b |
| Provider | Meta |
| Model type | Multimodal LLM |
| Context window | 164K catalog |
| Input modalities | image · text |
| Output modalities | text |
| Released | Apr 30, 2025 |
| Moderated | No |
| Architecture modality | text+image->text |
Pricing
Live pricing components for meta-llama/llama-guard-4-12b as published in the catalog.
| Pricing component | Raw unit price | Normalized | Unit |
|---|---|---|---|
| Prompt tokens | $1.8e-7 | $0.18 / 1M | Per input token |
| Completion tokens | $1.8e-7 | $0.18 / 1M | Per output token |
| Request fee | N/A | N/A | Per request |
| Image fee | N/A | N/A | Per image unit |
| Web search fee | N/A | N/A | Per search request |
Cost calculator
Estimate monthly spend from your own token volumes.
Estimated Total Cost
$0.13Calculated from current list pricing in the ModelsAtlas catalog.Capabilities
| Capability | Status | What it means |
|---|---|---|
| Visual Understanding | ✓ Supported | Image and document analysis support |
| Audio Processing | Not advertised | Speech and voice aligned flows |
| Tool Calling | Not advertised | Supports tools / function calling |
| Self-Hosting | ✓ Supported | Deploy outside managed APIs |
Capability signals
Directional signals derived from model metadata and capability tags — not official benchmark submissions.
Quick start
Call Meta: Llama Guard 4 12B through an OpenAI-compatible client.
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="meta-llama/llama-guard-4-12b",
messages=[{"role": "user", "content": "Explain quantum physics."}]
)
print(response.choices[0].message.content)Alternatives
Closest models by context window from a different provider.
| Model | Provider | Context | Input /1M | Actions |
|---|---|---|---|---|
| DeepSeek Chat (legacy id) | DeepSeek | 164K | $0.32 | View → |
| DeepSeek: DeepSeek V3 0324 | DeepSeek | 164K | $0.20 | View → |
Sources & attribution
Pricing and metadata are maintained in the ModelsAtlas catalog.
Frequently asked questions
How much does Meta: Llama Guard 4 12B cost?
$0.18 per 1M input tokens and $0.18 per 1M output tokens on the official Meta API.
What is the context window of Meta: Llama Guard 4 12B?
Meta: Llama Guard 4 12B supports up to 164K tokens of context.
Does Meta: Llama Guard 4 12B support tool / function calling?
Meta: Llama Guard 4 12B does not advertise first-class tool/function calling. Check vendor docs for the latest capabilities.
How do I access Meta: Llama Guard 4 12B?
Use the official Meta API with the model id `meta-llama/llama-guard-4-12b`. See the Quick start section above for code examples.