Context Window

Compare

Llama 4 Scout 17b 16e vs Meta Llama3 1 8b 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 4 Scout 17b 16e

Image inputTool calling

Context window

10M

10,000,000 tokens · ~7.5M words

Model page
Meta

Model

Meta Llama3 1 8b Instruct

Tool 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 4 Scout 17b 16e10M
Meta Llama3 1 8b Instruct128K

Llama 4 Scout 17b 16e has about 78.1× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 4 Scout 17b 16e has 7712% more context capacity (10000K vs 128K tokens). Llama 4 Scout 17b 16e is 77% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama 4 Scout 17b 16e. Its 10000K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Llama 4 Scout 17b 16e. Input tokens are 77% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Llama 4 Scout 17b 16e. 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 Scout 17b 16eMeta Llama3 1 8b Instruct
Context window10,000,000 tokens (10000K)128,000 tokens (128K)
Max output tokens16,384 tokens (16K)2,048 tokens (2K)
Speed tierFastFast
VisionYesNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AN/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 17b 16e inLlama 4 Scout 17b 16e outMeta Llama3 1 8b Instruct inMeta Llama3 1 8b Instruct out
Aws Bedrock——$0.220/M$0.220/M
Azure$0.200/M$0.780/M——
Deepinfra$0.080/M$0.300/M——
Groq$0.110/M$0.340/M——
Lambda$0.050/M$0.100/M——
Novita$0.180/M$0.590/M——
Nscale$0.090/M$0.290/M——
Sambanova$0.400/M$0.700/M——
Together Ai$0.180/M$0.590/M——

Frequently asked questions

Llama 4 Scout 17b 16e has a larger context window: 10000K 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.

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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