Context Window

Compare

Command A Plus 05 2026 vs Nano Banana 2.1

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.

Cohere

Model

Command A Plus 05 2026

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Google

Model

Nano Banana 2.1

Image inputTool calling

Context window

66K

65,536 tokens · ~49K 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.

Command A Plus 05 2026128K
Nano Banana 2.166K

Command A Plus 05 2026 has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Command A Plus 05 2026 has 95% more context capacity (128K vs 65K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Command A Plus 05 2026. Its 128K context fits entire documents without chunking (vs 65K).

  • Long output (reports, code files)

    Use Command A Plus 05 2026. Its 64K 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.

SpecCommand A Plus 05 2026Nano Banana 2.1
Context window128,000 tokens (128K)65,536 tokens (65K)
Max output tokens64,000 tokens (64K)58,982 tokens (58K)
Speed tierBalancedFast
VisionYesYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AOct 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.

ProviderCommand A Plus 05 2026 inCommand A Plus 05 2026 outNano Banana 2.1 inNano Banana 2.1 out
Cohere————
Openrouter——$1.50/M$7.50/M

Frequently asked questions

Command A Plus 05 2026 has a larger context window: 128K 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.

Powered by Mem0

Use a smaller model.
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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