Context Window

Compare

GPT-4o Search Preview 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.

Openai

Model

GPT-4o Search Preview

Image inputTool 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.

GPT-4o Search Preview128K
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 98% 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 98% cheaper — critical when sending large retrieved contexts.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecGPT-4o Search PreviewLlama 4 Scout 17b 16e
Context window128,000 tokens (128K)10,000,000 tokens (10000K)
Max output tokens16,384 tokens (16K)16,384 tokens (16K)
Speed tierBalancedFast
VisionYesYes
Function callingYesYes
Extended thinkingNoNo
Prompt cachingYesNo
Batch APIYesNo
Release dateMar 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.

ProviderGPT-4o Search Preview inGPT-4o Search Preview 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
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
Openai$2.50/M$10.00/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