Context Window

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Gpt 4o Mini Audio Preview vs Llama 4 Maverick

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 Mini Audio Preview

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Meta

Model

Llama 4 Maverick

Image inputTool calling

Context window

1.0M

1,048,576 tokens · ~786K 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 Mini Audio Preview128K
Llama 4 Maverick1.0M

Llama 4 Maverick has about 8.2× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 4 Maverick has 719% more context capacity (1048K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama 4 Maverick. Its 1048K context fits entire documents without chunking (vs 128K).

Full specs

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

SpecGpt 4o Mini Audio PreviewLlama 4 Maverick
Context window128,000 tokens (128K)1,048,576 tokens (1048K)
Max output tokens16,384 tokens (16K)16,384 tokens (16K)
Speed tierFastBalanced
VisionNoYes
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
Release dateN/AApr 2025

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 Mini Audio Preview inGpt 4o Mini Audio Preview outLlama 4 Maverick inLlama 4 Maverick out
Openai$0.150/M$0.600/M——

Frequently asked questions

Llama 4 Maverick has a larger context window: 1048K 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