Context Window

Compare

Llama 4 Scout vs Phi 3 Small 8k

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 4 Scout

Image inputTool calling

Context window

328K

327,680 tokens · ~246K words

Model page
Microsoft

Model

Phi 3 Small 8k

Context window

8K

8,192 tokens · ~6K 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 4 Scout328K
Phi 3 Small 8k8K

Llama 4 Scout has about 40× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 4 Scout has 3900% more context capacity (327K vs 8K tokens).

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 8K).

  • Long output (reports, code files)

    Use Llama 4 Scout. Its 16K 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 4 ScoutPhi 3 Small 8k
Context window327,680 tokens (327K)8,192 tokens (8K)
Max output tokens16,384 tokens (16K)4,096 tokens (4K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesNo
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 4 Scout inLlama 4 Scout outPhi 3 Small 8k inPhi 3 Small 8k out
Azure——$0.150/M$0.600/M

Frequently asked questions

Llama 4 Scout has a larger context window: 327K tokens vs 8K. 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.
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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