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Nano Banana 2 (Gemini 3.1 Flash Image) vs Gemini 3 1 Flash Live Preview

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

Nano Banana 2 (Gemini 3.1 Flash Image)

Image input

Context window

131K

131,072 tokens · ~98K words

Model page
Google

Model

Gemini 3 1 Flash Live Preview

Image inputTool calling

Context window

131K

131,072 tokens · ~98K 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.

Nano Banana 2 (Gemini 3.1 Flash Image)131K
Gemini 3 1 Flash Live Preview131K

Same context window size for both models.

Nano Banana 2 (Gemini 3.1 Flash Image) and Gemini 3 1 Flash Live Preview have identical context windows (131K tokens).

Full specs

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

SpecNano Banana 2 (Gemini 3.1 Flash Image)Gemini 3 1 Flash Live Preview
Context window131,072 tokens (131K)131,072 tokens (131K)
Max output tokens65,536 tokens (65K)65,536 tokens (65K)
Speed tierFastFast
VisionYesYes
Function callingNoYes
Extended thinkingYesNo
Prompt cachingNoNo
Batch APINoNo
Release dateJun 2026N/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.

ProviderNano Banana 2 (Gemini 3.1 Flash Image) inNano Banana 2 (Gemini 3.1 Flash Image) outGemini 3 1 Flash Live Preview inGemini 3 1 Flash Live Preview out
Google$0.750/M$4.50/M

Frequently asked questions

Gemini 3 1 Flash Live Preview has a larger context window: 131K tokens vs 131K. 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.
Get better results.

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.

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