Model Overview
Meta · Active Model
- Long-Context
- API Available
- Vetted Benchmarks
- Production Ready
Llama Guard 4 12B by Meta — Technical Architecture, Empirical Benchmarks & API Specs§
1. Executive Summary & Core Positioning§
Llama Guard 4 12B is an advanced artificial intelligence model engineered by Meta. Tailored for complex multi-step reasoning, programming synthesis, and extended document comprehension, Llama Guard 4 12B represents a key architectural iteration in the Meta model family. First released in 2025-04-30, it serves enterprise developers, research teams, and autonomous system architects requiring strict instruction compliance.
Featuring an input capacity of 163,840 tokens (approximately 218 words), Llama Guard 4 12B processes multi-file code repositories, lengthy technical reports, and complex prompts in a single inference call.
2. Technical Architecture & Verified Specifications§
Official specification breakdown for Llama Guard 4 12B based on verified provider metadata:
- Model Name: Llama Guard 4 12B
- Developer / Provider: Meta
- Context Window Capacity: 163,840 tokens (~218 words)
- Modality Support: Text, Code
- API Availability: Available via API Gateway
- Tool-Calling Accuracy Score: Pending Empirical Benchmark
- Time to First Token (TTFT): Varies by Host Provider
3. Benchmark Evaluations & Performance Metrics§
Llama Guard 4 12B undergoes standardized evaluation across key industry benchmark suites:
- MMLU (Massive Multitask Language Understanding): Evaluates multi-subject knowledge across STEM, humanities, and social sciences.
- HumanEval & SWE-bench: Assesses functional Python code synthesis and real-world software engineering bug resolution.
- GSM8K & MATH: Tests multi-step arithmetic reasoning and formal mathematical proof construction.
- Chatbot Arena ELO: Evaluates human preference, instruction following, and conversational quality against rival models.
4. Developer API & Integration Specs§
Programmatic integration for Llama Guard 4 12B follows standard OpenAI-compatible REST endpoints.
import os
import requests
api_key = os.getenv("MODEL_API_KEY")
url = "https://openrouter.ai/api/v1/chat/completions"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
payload = {
"model": "llama-guard-4-12b",
"messages": [
{"role": "system", "content": "You are a senior software architect and AI system evaluator."},
{"role": "user", "content": "Analyze system architecture bottlenecks and suggest refactoring strategies."}
],
"temperature": 0.1,
"max_tokens": 2048
}
response = requests.post(url, headers=headers, json=payload)
print(response.json())5. Production Use Cases & Deployment Scenarios§
5.1 Autonomous Agents & Tool Execution§
Given its instruction compliance and tool-calling capabilities (Pending Empirical Benchmark), Llama Guard 4 12B is frequently integrated as the reasoning engine for autonomous software agents, browser automation pipelines, and API orchestrators.
5.2 Enterprise Document Synthesis§
With its 163,840 tokens input capacity, engineering and legal teams process full regulatory filings, annual corporate disclosures, and technical documentation directly without context loss.
5.3 Programmatic Code Generation§
Development teams utilize Llama Guard 4 12B for automated code generation, pull request audits, unit test suite creation, and language migration (e.g. Python to Rust).
6. Token Economics & Pricing Breakdown§
Inference pricing per million tokens for Llama Guard 4 12B:
- Input Token Cost: $0.18 per MTok
- Output Token Cost: $0.18 per MTok
- Prompt Caching Discounts: Supported on selected API providers (up to 50% savings on repeated prompt prefixes)
- Batch Processing: Available for non-latency-sensitive bulk inference workloads
7. Comparative Specification Matrix§
| Metric / Parameter | **Llama Guard 4 12B** | Provider Ecosystem Baseline |
|---|---|---|
| Developer | Meta | Industry Average |
| Context Window | 163,840 tokens | 128,000 tokens |
| Input Price / MTok | $0.18 | Variable |
| Output Price / MTok | $0.18 | Variable |
| API Access | Supported | Standard |
8. Frequently Asked Questions (FAQ)§
Q: What is Llama Guard 4 12B's context window limit?§
A: Llama Guard 4 12B supports an input context window of 163,840 tokens.
Q: What is the API pricing for Llama Guard 4 12B?§
A: Llama Guard 4 12B is priced at $0.18 per million input tokens and $0.18 per million output tokens.
Q: Is Llama Guard 4 12B accessible via API?§
A: Yes, Llama Guard 4 12B is available for programmatic integration.
- Developer
- Meta✓ verified today
- Release Date
- April 30, 2025✓ verified today
- Context Window
- 163,840 tokens≈ 218 words✓ verified today
- API Access
- Publicly AvailableIntegrate via official API✓ verified today
- Input Cost
- $0.18per million tokens✓ verified today
- Output Cost
- $0.18per million tokens✓ verified today