Context Window

Compare

Devstral Small 2505 vs Llama 4 Scout 17b 16e

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.

Mistral

Model

Devstral Small 2505

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Meta

Model

Llama 4 Scout 17b 16e

Image inputTool 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.

Devstral Small 2505128K
Llama 4 Scout 17b 16e10M

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 50% 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 50% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Devstral Small 2505. 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.

SpecDevstral Small 2505Llama 4 Scout 17b 16e
Context window128,000 tokens (128K)10,000,000 tokens (10000K)
Max output tokens128,000 tokens (128K)16,384 tokens (16K)
Speed tierBalancedFast
VisionNoYes
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.

ProviderDevstral Small 2505 inDevstral Small 2505 outLlama 4 Scout 17b 16e inLlama 4 Scout 17b 16e out
Azure——$0.200/M$0.780/M
Deepinfra——$0.080/M$0.300/M
Fireworks$0.900/M$0.900/M——
Groq——$0.110/M$0.340/M
Lambda——$0.050/M$0.100/M
Mistral$0.100/M$0.300/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.
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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