Context Window
163,840
Tokens
Model Cost Profile
Meta: Llama Guard 4 12B
Developer: meta-llama
Pricing updated Mar 11, 2026
Input rank: #127Output rank: #61
Meta: Llama Guard 4 12B, developed by meta-llama, offers a substantial context window of 163,840 tokens, making it suitable for applications requiring extensive text analysis and generation, such as legal document review or long-form content creation. With a competitive pricing structure of $0.18 per million tokens for both input and output, teams can effectively manage costs while leveraging the model for high-volume tasks. This model is particularly advantageous for organizations needing to process large datasets or engage in complex conversational AI scenarios.
👁 Vision📋 Structured Output
Input Price / 1M
$0.1800
Prompt tokens
Output Price / 1M
$0.1800
Completion tokens
Intelligence (MMLU)
Benchmark Pending
Massive Multitask Language Understanding
Side-by-Side Pricing Table
| Usage Type | Price / 1M Tokens |
|---|---|
| Input (Prompt) | $0.1800 |
| Output (Completion) | $0.1800 |
Price History
Meta: Llama Guard 4 12B Pricing Trend
Input / 1M tokens0.0%Output / 1M tokens0.0%
Mar 7 — Mar 11
Current Input / 1M
$0.1800
Current Output / 1M
$0.1800
Cost Calculator
Estimate monthly spend for Meta: Llama Guard 4 12B based on your workload.
01.0B
0500M
Estimated Monthly Cost
$6.66
25M input + 12M output tokens
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FAQ
Common pricing and benchmark questions for Meta: Llama Guard 4 12B.
How much does Meta: Llama Guard 4 12B cost per 1M input tokens?
Meta: Llama Guard 4 12B input pricing is $0.1800 per 1M tokens based on the latest synced provider data.
How much does Meta: Llama Guard 4 12B cost per 1M output tokens?
Meta: Llama Guard 4 12B output pricing is $0.1800 per 1M tokens based on the latest synced provider data.
What context window does Meta: Llama Guard 4 12B support?
Meta: Llama Guard 4 12B supports a context window of 163,840 tokens.
How can I compare Meta: Llama Guard 4 12B with cheaper alternatives?
Use the comparison links on this page to open direct model-vs-model pricing and benchmark pages, then evaluate monthly spend projections for your workload.