Context Window

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Gemini Omni Flash Preview vs Llama 3.2 1B Instruct

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

Gemini Omni Flash Preview

Image input

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Meta

Model

Llama 3.2 1B Instruct

Context window

60K

60,000 tokens · ~45K 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.

Gemini Omni Flash Preview1.0M
Llama 3.2 1B Instruct60K

Gemini Omni Flash Preview has about 17.5× the context window of the other in this pair.

Token calculator →Memory savings →

Gemini Omni Flash Preview has 1647% more context capacity (1048K vs 60K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gemini Omni Flash Preview. Its 1048K context fits entire documents without chunking (vs 60K).

Full specs

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

SpecGemini Omni Flash PreviewLlama 3.2 1B Instruct
Context window1,048,576 tokens (1048K)60,000 tokens (60K)
Max output tokens65,535 tokens (65K)N/A
Speed tierFastFast
VisionYesNo
Function callingNoNo
Extended thinkingYesNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/ASep 2024

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.

ProviderGemini Omni Flash Preview inGemini Omni Flash Preview outLlama 3.2 1B Instruct inLlama 3.2 1B Instruct out
Google$1.50/M$9.00/M——
Google Vertex$1.50/M$9.00/M——

Frequently asked questions

Gemini Omni Flash Preview has a larger context window: 1048K tokens vs 60K. 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