Context Window

Compare

Grok 4.5 vs Llama Prompt Guard 2 22m

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.

Xai

Model

Grok 4.5

Image inputTool calling

Context window

500K

500,000 tokens · ~375K words

Model page
Meta

Model

Llama Prompt Guard 2 22m

Context window

1K

512 tokens · ~384 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.

Grok 4.5500K
Llama Prompt Guard 2 22m1K

Grok 4.5 has about 976.6× the context window of the other in this pair.

Token calculator →Memory savings →

Grok 4.5 has 97556% more context capacity (500K vs 0K tokens). Llama Prompt Guard 2 22m is 98% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Grok 4.5. Its 500K context fits entire documents without chunking (vs 0K).

  • RAG / high-volume retrieval

    Use Llama Prompt Guard 2 22m. Input tokens are 98% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGrok 4.5Llama Prompt Guard 2 22m
Context window500,000 tokens (500K)512 tokens (0K)
Max output tokensN/A512 tokens (0K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateJul 2026N/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.

ProviderGrok 4.5 inGrok 4.5 outLlama Prompt Guard 2 22m inLlama Prompt Guard 2 22m out
Groq——$0.030/M$0.030/M
Xai$2.00/M$6.00/M——

Frequently asked questions

Grok 4.5 has a larger context window: 500K tokens vs 0K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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Use a smaller model.
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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.

Calculate your savingsLearn about Mem0

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