Context Window

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Devstral 2 2512 vs Google Gemma 4 26b A4b

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 2 2512

Tool calling

Context window

256K

256,000 tokens · ~192K words

Model page
Google

Model

Google Gemma 4 26b A4b

Image inputTool calling

Context window

256K

256,000 tokens · ~192K 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 2 2512256K
Google Gemma 4 26b A4b256K

Same context window size for both models.

Token calculator →Memory savings →

Devstral 2 2512 and Google Gemma 4 26b A4b have identical context windows (256K tokens). Google Gemma 4 26b A4b is 67% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Google Gemma 4 26b A4b. Input tokens are 67% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecDevstral 2 2512Google Gemma 4 26b A4b
Context window256,000 tokens (256K)256,000 tokens (256K)
Max output tokens256,000 tokens (256K)256,000 tokens (256K)
Speed tierBalancedBalanced
VisionNoYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingYesNo
Batch APINoNo
Release dateDec 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.

ProviderDevstral 2 2512 inDevstral 2 2512 outGoogle Gemma 4 26b A4b inGoogle Gemma 4 26b A4b out
Aws Bedrock——$0.130/M$0.400/M
Mistral$0.400/M$2.00/M——
Openrouter$0.150/M$0.600/M——

Frequently asked questions

Google Gemma 4 26b A4b has a larger context window: 256K tokens vs 256K. 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