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Meta: Llama Guard 4 12B

Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. $0.18 per million input tokens, $0.18 per million output tokens. 163,840 token context window, maximum output of 16,384 tokens.

View source on OpenRouter Hugging Face
Overview

Model specifications

Context
163,840 tokens
Maximum output
16,384 tokens
Architecture
text+image->text
Tokenizer
Other
Knowledge cutoff
2024-08-31
Moderated
No
OPENROUTER

Complete pricing

Synchronized OpenRouter rates. Token prices are shown per one million tokens.

Input
$0.18
/M tokens
Output
$0.18
/M tokens
API

Quick start

Call this exact model ID through OpenRouter’s OpenAI-compatible API.

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"meta-llama/llama-guard-4-12b","messages":[{"role":"user","content":"Hello!"}]}'
Capabilities

Input → Output

Input
imagetext
Output
text
API

Supported API parameters

frequency_penaltylogit_biasmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstoptemperaturetop_ktop_p
Available providers

1 Provider

Live providers on OpenRouter

Provider availability, latency, throughput and routing can change continuously. Open the source page for current operational data.

OpenRouter

DeepInfra

bf16
Context
164K
Maximum output
16K
Input
$0.180
Output
$0.180
Cache read
Not provided
Cache write
Not provided
meta-llama

models

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Meta: Llama 4 Maverick

Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...

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Meta: Llama 4 Scout

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Meta: Llama 3.3 70B Instruct

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model...

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Meta: Llama 3.2 1B Instruct

Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate...

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Output
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Input: $0.027Output: $0.201per 1M tokens
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