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

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

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Open-Weight Models · verified 20 Oct 2026

DeepSeek V4.1 FlashDeepSeekcurrent

XMiMo-V2.6-ProXiaomicurrent

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Specification	DeepSeek V4.1 Flash	MiMo-V2.6-Pro
Summary	Twice the parameters of the Flash it replaces, and cheaper to run.	A trillion parameters under the MIT licence.
Context	1M	1M
Input ($/Mtok)	$0.3	Weights only
Output ($/Mtok)	$1.2	Weights only
Licence	MIT	MIT
Max output	256K	Not published
Parameters	552B total, 8B / 16B active	1.02T total, 42B active
Released	10 Sep 2026, refreshed 17 Oct	21 Sep 2026
Model ID	deepseek-flash	XiaomiMiMo/MiMo-V2.6-Pro
Category	Open-Weight Models	Open-Weight Models
Official	DeepSeek ↗	Xiaomi ↗

Highlighted rows are where these differ.

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
Refreshed on 17 October as V4.1-Flash-1017, still MIT, improving on all eleven benchmarks DeepSeek published

Best for cheap agentic coding with images.

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.