Context Window

Compare

Devstral Latest vs Nano Banana Pro (Gemini 3 Pro Image)

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.

Mistral

Model

Devstral Latest

Tool calling

Context window

256K

256,000 tokens · ~192K words

Model page
Google

Model

Nano Banana Pro (Gemini 3 Pro Image)

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.

Devstral Latest256K
Nano Banana Pro (Gemini 3 Pro Image)66K

Devstral Latest has about 3.9× the context window of the other in this pair.

Token calculator →Memory savings →

Devstral Latest has 290% more context capacity (256K vs 65K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Devstral Latest. Its 256K context fits entire documents without chunking (vs 65K).

  • Long output (reports, code files)

    Use Devstral Latest. Its 256K 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.

SpecDevstral LatestNano Banana Pro (Gemini 3 Pro Image)
Context window256,000 tokens (256K)65,536 tokens (65K)
Max output tokens256,000 tokens (256K)32,768 tokens (32K)
Speed tierBalancedFast
VisionNoYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/AJun 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.

ProviderDevstral Latest inDevstral Latest outNano Banana Pro (Gemini 3 Pro Image) inNano Banana Pro (Gemini 3 Pro Image) out
Mistral$0.400/M$2.00/M——

Frequently asked questions

Devstral Latest has a larger context window: 256K 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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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