MiMo-V2.6-Pro vs DeepSeek V4.1 Flash

Pyyan / Compare / MiMo-V2.6-Pro vs DeepSeek V4.1 Flash

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Open-Weight Models · verified 29 Sept 2026

XMiMo-V2.6-ProXiaomicurrent

DeepSeek V4.1 FlashDeepSeekcurrent

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Specification	MiMo-V2.6-Pro	DeepSeek V4.1 Flash
Summary	A trillion parameters under the MIT licence.	Twice the parameters of the Flash it replaces, and cheaper to run.
Context	1M	1M
Input ($/Mtok)	Weights only	$0.3
Output ($/Mtok)	Weights only	$1.2
Licence	MIT	MIT
Max output	Not published	256K
Parameters	1.02T total, 42B active	552B total, 8B / 16B active
Released	21 Sep 2026	10 Sep 2026
Model ID	XiaomiMiMo/MiMo-V2.6-Pro	deepseek-flash
Category	Open-Weight Models	Open-Weight Models
Official	Xiaomi ↗	DeepSeek ↗

Highlighted rows are where these differ.

1.02T total parameters, 42B active per token, a 4.1% activation ratio
Text, image, video and audio in, with a 1M token context
Shipped with 7,000+ reinforcement learning environments and the framework that trained it

Best for multimodal work on your own cluster.

552B total, 8B active while reading the prompt and 16B while generating, with a 1M token context
DeepSeek says it beats V4-Pro; V4-Pro requests route to it from 14 September 2026
MIT licence, native image input, and a KV cache about a quarter the size of V4-Flash's

Best for cheap agentic coding with images.