GROQ MODEL TOKEN COUNTER

llama-guard-4-12b Token Counter

Count tokens for llama-guard-4-12b and estimate API cost before you send a request. Groq does not publish this tokenizer, so counts are calibrated estimates for planning. Your text never leaves the browser.

PROMPT

Paste text to count llama-guard-4-12b tokens.

Tokens

0

Words

0

Characters

0

Input cost

$0.00

Output cost

$0.00

Total request

$0.00

8,192 token context window 0%

Need optimization suggestions, a token heatmap, monthly cost projection, or a side-by-side model comparison? Open the full analyzer with llama-guard-4-12b preselected.

llama-guard-4-12b at a glance

Provider. Groq

Context window. 8,192 tokens (about 6,000 English words).

API pricing. $0.20 input / $0.20 output per 1M tokens.

Tokenizer. Not public; counts on this page are calibrated estimates.

Pricing source. The community-maintained LiteLLM pricing dataset, refreshed 2026-07-01. Community-tracked rates can lag provider changes; confirm with the provider before budgeting.

FAQ

llama-guard-4-12b token counter FAQ

How do I count tokens for llama-guard-4-12b?

Paste your prompt into the counter on this page. Groq does not publish the llama-guard-4-12b tokenizer, so the count is a calibrated estimate based on text length, language, and structure. Treat it as planning guidance rather than an exact billing number.

How much does llama-guard-4-12b cost per token?

llama-guard-4-12b costs $0.20 per 1 million input tokens and $0.20 per 1 million output tokens, based on the community-maintained LiteLLM pricing dataset checked 2026-07-01.

What is the context window of llama-guard-4-12b?

llama-guard-4-12b has a context window of 8,192 tokens, which is roughly 6,000 English words of combined prompt and response. The counter above shows how much of that window your text consumes.

Is this llama-guard-4-12b token count exact?

No. It is a clearly labeled estimate, because Groq has not released a public tokenizer for llama-guard-4-12b. Estimates are usually within a reasonable range for English text but can drift for code, JSON, or non-English languages.