Context Window

Compare

Gemma 3 4b It Gguf vs Llama 4 Scout 17b 16e Instruct Fp8

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.

Google

Model

Gemma 3 4b It Gguf

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Meta

Model

Llama 4 Scout 17b 16e Instruct Fp8

Tool calling

Context window

10M

10,000,000 tokens · ~7.5M 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.

Gemma 3 4b It Gguf128K
Llama 4 Scout 17b 16e Instruct Fp810M

Llama 4 Scout 17b 16e Instruct Fp8 has about 78.1× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 4 Scout 17b 16e Instruct Fp8 has 7712% more context capacity (10000K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama 4 Scout 17b 16e Instruct Fp8. Its 10000K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Gemma 3 4b It Gguf. Its 8K 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.

SpecGemma 3 4b It GgufLlama 4 Scout 17b 16e Instruct Fp8
Context window128,000 tokens (128K)10,000,000 tokens (10000K)
Max output tokens8,192 tokens (8K)4,028 tokens (4K)
Speed tierBalancedFast
VisionNoNo
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.

ProviderGemma 3 4b It Gguf inGemma 3 4b It Gguf outLlama 4 Scout 17b 16e Instruct Fp8 inLlama 4 Scout 17b 16e Instruct Fp8 out
Lemonade————
Meta————

Frequently asked questions

Llama 4 Scout 17b 16e Instruct Fp8 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.

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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