Compare

Llama Guard 4 12B (free) vs Meta Llama3 2 11b Instruct

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Meta

Model

Llama Guard 4 12B (free)

Image input

Context window

164K

163,840 tokens · ~123K words

Model page
Meta

Model

Meta Llama3 2 11b Instruct

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

Llama Guard 4 12B (free)164K
Meta Llama3 2 11b Instruct128K

Llama Guard 4 12B (free) has about 1.3× the context window of the other in this pair.

Llama Guard 4 12B (free) has 28% more context capacity (163K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama Guard 4 12B (free). Its 163K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Llama Guard 4 12B (free). Its 65K max output lets you generate complete artifacts in one request.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecLlama Guard 4 12B (free)Meta Llama3 2 11b Instruct
Context window163,840 tokens (163K)128,000 tokens (128K)
Max output tokens65,000 tokens (65K)4,096 tokens (4K)
Speed tierBalancedFast
VisionYesYes
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateApr 2025N/A

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderLlama Guard 4 12B (free) inLlama Guard 4 12B (free) outMeta Llama3 2 11b Instruct inMeta Llama3 2 11b Instruct out
Aws Bedrock$0.350/M$0.350/M

Frequently asked questions

Llama Guard 4 12B (free) has a larger context window: 163K tokens vs 128K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model