Context Window

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Nano Banana Pro (Gemini 3 Pro Image Preview) vs Glm 5p1

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 Preview)

Image input

Context window

66K

65,536 tokens · ~49K words

Model page
Z Ai

Model

Glm 5p1

Context window

203K

202,800 tokens · ~152K 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 Preview)66K
Glm 5p1203K

Glm 5p1 has about 3.1× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 5p1 has 209% more context capacity (202K vs 65K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 5p1. Its 202K context fits entire documents without chunking (vs 65K).

  • Long output (reports, code files)

    Use Glm 5p1. Its 202K 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.

SpecNano Banana Pro (Gemini 3 Pro Image Preview)Glm 5p1
Context window65,536 tokens (65K)202,800 tokens (202K)
Max output tokens32,768 tokens (32K)202,800 tokens (202K)
Speed tierFastBalanced
VisionYesNo
Function callingNoNo
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateNov 2025N/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 Pro (Gemini 3 Pro Image Preview) inNano Banana Pro (Gemini 3 Pro Image Preview) outGlm 5p1 inGlm 5p1 out
Fireworks——$1.40/M$4.40/M

Frequently asked questions

Glm 5p1 has a larger context window: 202K 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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Example: a multi-turn chat session

Without Mem0~128K tokens sent
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Repeated info
Old context
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