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Gemma 4 26B A4B vs Kimi K2.6 (free)

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

Gemma 4 26B A4B

Image inputTool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Moonshot

Model

Kimi K2.6 (free)

Image inputTool calling

Context window

262K

262,144 tokens · ~197K 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.

Gemma 4 26B A4B262K
Kimi K2.6 (free)262K

Same context window size for both models.

Gemma 4 26B A4B and Kimi K2.6 (free) have identical context windows (262K tokens).

Full specs

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

SpecGemma 4 26B A4BKimi K2.6 (free)
Context window262,144 tokens (262K)262,144 tokens (262K)
Max output tokens262,144 tokens (262K)N/A
Speed tierBalancedBalanced
VisionYesYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingNoNo
Batch APINoNo
Release dateApr 2026Apr 2026

Frequently asked questions

Kimi K2.6 (free) has a larger context window: 262K tokens vs 262K. 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.
Get better results.

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.

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