Context Window

Compare

Llama 4 Scout vs Open Mistral Nemo

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
Mistral

Model

Open Mistral Nemo

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 Scout328K
Open Mistral Nemo128K

Llama 4 Scout has about 2.6× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 4 Scout has 156% more context capacity (327K vs 128K 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 128K).

  • Long output (reports, code files)

    Use Open Mistral Nemo. Its 128K 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 ScoutOpen Mistral Nemo
Context window327,680 tokens (327K)128,000 tokens (128K)
Max output tokens16,384 tokens (16K)128,000 tokens (128K)
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 outOpen Mistral Nemo inOpen Mistral Nemo out
Mistral——$0.300/M$0.300/M

Frequently asked questions

Llama 4 Scout has a larger context window: 327K 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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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