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

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

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Open-Weight Models · verified 29 Sept 2026

XMiMo-V2.6-ProXiaomicurrent
DeepSeek V4-ProDeepSeekcurrent
3 slots left
SpecificationMiMo-V2.6-ProDeepSeek V4-Pro
SummaryA trillion parameters under the MIT licence.Best all-round open model of 2026.
Context1M—
Input ($/Mtok)Weights only—
Output ($/Mtok)Weights only—
LicenceMITMIT
Max outputNot published—
Parameters1.02T total, 42B active—
Released21 Sep 2026—
Model IDXiaomiMiMo/MiMo-V2.6-Pro—
CategoryOpen-Weight ModelsOpen-Weight Models
OfficialXiaomi ↗DeepSeek ↗

Highlighted rows are where these differ.

MiMo-V2.6-Pro

  • 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.

Full spec sheet →

DeepSeek V4-Pro

  • Tops open leaderboards on agentic coding and reasoning
  • MIT licence — no conditions
  • Pioneered aggressive cache-hit pricing at ~$0.07/M
  • DeepSeek routes all V4-Pro requests to V4.1 Flash from 14 September 2026

Best for everything open.

Full spec sheet →