Context Window

Compare

Gemini Omni 1 1 Flash Preview vs MiMo-V2-Omni

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 1 1 Flash Preview

Image input

Context window

58K

57,920 tokens · ~43K words

Model page
Xiaomi

Model

MiMo-V2-Omni

Image inputTool calling

Context window

262K

262,144 tokens · ~197K 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 1 1 Flash Preview58K
MiMo-V2-Omni262K

MiMo-V2-Omni has about 4.5× the context window of the other in this pair.

Token calculator →Memory savings →

MiMo-V2-Omni has 352% more context capacity (262K vs 57K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use MiMo-V2-Omni. Its 262K context fits entire documents without chunking (vs 57K).

  • Long output (reports, code files)

    Use MiMo-V2-Omni. Its 65K 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.

SpecGemini Omni 1 1 Flash PreviewMiMo-V2-Omni
Context window57,920 tokens (57K)262,144 tokens (262K)
Max output tokens57,920 tokens (57K)65,536 tokens (65K)
Speed tierFastBalanced
VisionYesYes
Function callingNoYes
Extended thinkingYesYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/AMar 2026

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 1 1 Flash Preview inGemini Omni 1 1 Flash Preview outMiMo-V2-Omni inMiMo-V2-Omni out
Google Vertex$1.50/M$9.00/M——

Frequently asked questions

MiMo-V2-Omni has a larger context window: 262K tokens vs 57K. 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
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Current turn

80% less to send — works with any model