PyyanThe AI Index

Pyyan / Compare / Qwen3.8-Flash-Next vs MiMo-V2.6-Pro

Qwen3.8-Flash-Next vs MiMo-V2.6-Pro

2 of 5

Open-Weight Models · verified 29 Sept 2026

Qwen3.8-Flash-NextAlibabacurrent
XMiMo-V2.6-ProXiaomicurrent
3 slots left
SpecificationQwen3.8-Flash-NextMiMo-V2.6-Pro
SummaryThe most downloaded thing on Hugging Face right now, and a stated Qwen4 preview.A trillion parameters under the MIT licence.
Context262K native, ~1M extended1M
Input ($/Mtok)$0.15Weights only
Output ($/Mtok)$0.47Weights only
LicenceOpen weightsMIT
Max output32KNot published
Parameters125B total, 6B active1.02T total, 42B active
Released26 Aug 202621 Sep 2026
Model IDQwen/Qwen3.8-Flash-NextXiaomiMiMo/MiMo-V2.6-Pro
CategoryOpen-Weight ModelsOpen-Weight Models
Official—Xiaomi ↗

Highlighted rows are where these differ.

Qwen3.8-Flash-Next

  • 125B total parameters with only 6B active per token
  • Alibaba describes it as a preview of the Qwen4 architecture
  • Top of the Hugging Face trending list, with a GGUF build close behind it

Best for open weights at speed.

Full spec sheet →

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 →