Context Window

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Aion-RP 1.0 (8B) vs Llama 4 Scout

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

Aion-RP 1.0 (8B)

Context window

33K

32,768 tokens · ~25K words

Model page
Meta

Model

Llama 4 Scout

Image inputTool calling

Context window

328K

327,680 tokens · ~246K 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.

Aion-RP 1.0 (8B)33K
Llama 4 Scout328K

Llama 4 Scout has about 10× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 4 Scout has 900% more context capacity (327K vs 32K tokens). Llama 4 Scout is 87% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama 4 Scout. Its 327K context fits entire documents without chunking (vs 32K).

  • RAG / high-volume retrieval

    Use Llama 4 Scout. Input tokens are 87% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Aion-RP 1.0 (8B). Its 29K 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.

SpecAion-RP 1.0 (8B)Llama 4 Scout
Context window32,768 tokens (32K)327,680 tokens (327K)
Max output tokens29,491 tokens (29K)16,384 tokens (16K)
Speed tierFastBalanced
VisionNoYes
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateFeb 2025Apr 2025

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.

ProviderAion-RP 1.0 (8B) inAion-RP 1.0 (8B) outLlama 4 Scout inLlama 4 Scout out
Openrouter$0.800/M$1.60/M$0.100/M$0.300/M

Frequently asked questions

Llama 4 Scout has a larger context window: 327K tokens vs 32K. 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.

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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