MiMo-V2.6-Flash is an open-source foundation model developed by Xiaomi. Built on a Mixture-of-Experts architecture with 309B total parameters and 15B activated per token, it employs a hybrid...
MiMo-V2.6-Pro is the flagship foundation model developed by Xiaomi. Built at a scale of over 1T parameters, it is designed to push the ceiling of capability for the most demanding workloads. The...
MiMo-V2.5-Pro is Xiaomi's latest flagship model, specifically re-engineered for stable operation over long, complex processes. It introduces the "Harness-Awareness" mechanism, enabling the model to...
MiMo-V2.5 is the versatile, high-efficiency backbone of Xiaomi's 2.5-series AI ecosystem. While inheriting the "Harness-Awareness" architecture from the Pro version, MiMo-V2.5 is optimized for lower...
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MiMo-V2.6-Flash is an open-source foundation model developed by Xiaomi. Built on a Mixture-of-Experts architecture with 309B total parameters and 15B activated per token, it employs a hybrid...
MiMo-V2.6-Pro is the flagship foundation model developed by Xiaomi. Built at a scale of over 1T parameters, it is designed to push the ceiling of capability for the most demanding workloads. The...
MiMo-V2.5-Pro is Xiaomi's latest flagship model, specifically re-engineered for stable operation over long, complex processes. It introduces the "Harness-Awareness" mechanism, enabling the model to...
MiMo-V2.5 is the versatile, high-efficiency backbone of Xiaomi's 2.5-series AI ecosystem. While inheriting the "Harness-Awareness" architecture from the Pro version, MiMo-V2.5 is optimized for lower...
MiMo-V2-Flash is an open-source foundation language model developed by Xiaomi. It is a Mixture-of-Experts model with 309B total parameters and 15B active parameters, adopting hybrid attention...
MiMo-V2-Omni is a frontier omni-modal model that natively processes image, video, and audio inputs within a unified architecture. It combines strong multimodal perception with agentic capability -...
MiMo-V2-Pro is Xiaomi's flagship foundation model, featuring over 1T total parameters and a 1M context length, deeply optimized for agentic scenarios. It is highly adaptable to general agent...