Context Window

Compare

Nano Banana Pro (Gemini 3 Pro Image) vs GPT-4.1

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 Pro (Gemini 3 Pro Image)

Image inputTool calling

Context window

66K

65,536 tokens · ~49K words

Model page
Openai

Model

GPT-4.1

Image inputTool calling

Context window

1.0M

1,047,576 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.

Nano Banana Pro (Gemini 3 Pro Image)66K
GPT-4.11.0M

GPT-4.1 has about 16× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-4.1 has 1498% more context capacity (1047K vs 65K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-4.1. Its 1047K context fits entire documents without chunking (vs 65K).

Full specs

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

SpecNano Banana Pro (Gemini 3 Pro Image)GPT-4.1
Context window65,536 tokens (65K)1,047,576 tokens (1047K)
Max output tokens32,768 tokens (32K)32,768 tokens (32K)
Speed tierFastBalanced
VisionYesYes
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesYes
Batch APINoNo
Release dateJun 2026Apr 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.

ProviderNano Banana Pro (Gemini 3 Pro Image) inNano Banana Pro (Gemini 3 Pro Image) outGPT-4.1 inGPT-4.1 out
Azure——$2.00/M$8.00/M
Openai——$2.00/M$8.00/M
Openrouter——$2.00/M$8.00/M
Replicate——$2.00/M$8.00/M

Frequently asked questions

GPT-4.1 has a larger context window: 1047K tokens vs 65K. 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