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Kimi K2.6 (free) vs MiMo-V2-Omni

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.

Moonshot

Model

Kimi K2.6 (free)

Image inputTool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Xiaomi

Model

MiMo-V2-Omni

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.

Kimi K2.6 (free)262K
MiMo-V2-Omni262K

Same context window size for both models.

Kimi K2.6 (free) and MiMo-V2-Omni have identical context windows (262K tokens).

Full specs

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

SpecKimi K2.6 (free)MiMo-V2-Omni
Context window262,144 tokens (262K)262,144 tokens (262K)
Max output tokensN/A65,536 tokens (65K)
Speed tierBalancedBalanced
VisionYesYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingNoYes
Batch APINoNo
Release dateApr 2026Mar 2026

Frequently asked questions

MiMo-V2-Omni 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