Xiaomi open sources MiMo-V2.5 and MiMo-V2.5-Pro under the MIT License, saying both models are among the most efficient available for agentic “claw” tasks
Xiaomi, the Chinese firm best known for its smartphones and electric vehicles, has lately been shipping some incredibly affordable …
VentureBeatCarl Franzen
Context & Ripple Effects
Xiaomi’s MiMo effort progressed from an open-source reasoning model to MiMo-V2, a 1T-parameter Pro variant, and an open-weight MoE release aimed at reasoning, coding, and agentic use. This release extends that sequence by making two newer variants available under a permissive license rather than limiting access to an API or a proprietary product.
The subsequent MiMo Code release and Xiaomi’s later speed claim show the company building a broader developer-facing model portfolio around agentic and coding workloads. The immediate significance is therefore not a single benchmark claim, but a more accessible distribution path for that portfolio.
First-order effects
Developers and enterprises can obtain, modify, and deploy MiMo-V2.5 and MiMo-V2.5-Pro under MIT terms, reducing dependence on Xiaomi-hosted access for these models.
Xiaomi gains a wider route to developer adoption for its agentic-model line; its efficiency positioning remains a company claim rather than an independently established result in this corpus.
Second-order effects
Organizations evaluating models for agentic workflows gain another open option, strengthening their leverage to compare self-hosted and externally provided models on deployment fit, cost, and performance.
Model vendors focused on closed access or hosted-only delivery face added pressure to differentiate through reliability, tooling, support, or proprietary capabilities, particularly as Xiaomi also extends MiMo into coding assistance.
Third-order effects
If major device and platform companies keep distributing capable models under permissive terms, model selection is likely to become more procurement-led: buyers can substitute among weights, hosted services, and specialized tooling rather than accept a single access model.
The durable divide may shift from access to model weights toward the operational capabilities needed to run and integrate them—hardware, inference efficiency, developer tools, and support—though the corpus does not establish which MiMo models will win adoption.
The trend: This is one instance of open-weight, agent-oriented model releases shifting competition from exclusive model access toward deployability and ecosystem execution.
Xiaomi's MiMo V2.5 Pro has landed at 54 in the Artificial Analysis Intelligence Index, tied with Moonshot's Kimi K2.6 - the current top open weights model. MiMo V2.5 Pro's weights are expected to be released soon, which would make MiMo V2.5 Pro the first equal open weights model …
Just dropped two open-source models: MiMo-V2.5-Pro (Code Agent, 1T total) and MiMo-V2.5 (Multimodal Agent, 310B total). Oh and one more thing — we're giving devs & creators 100T tokens on us. Go build something cool 🛠️ 🎁 100T Free Token Grant for Builders
SGLang and vLLM support for the MiMo-V2.5 series is here. 🙌 Huge thanks to SGLang project from @lmsysorg and @vllm_project for moving fast and helping developers get started with MiMo-V2.5 on day zero. [image]
xiaomi mimo v2.5 eval card, pro is 1T total 42B active, omni (video/image/audio) is 310B total 15B active, both have 1M context support they train in FP8, 27T tokens for pro and 48T for the smaller variant. interleaved SWA with an aggressive 6:1 ratio and 128 window size, still […
New Opensource SoTA contender enters the arena Xiaomi MiMo-V2.5 Pro - 1.02T Total Params / 42B Active Params - Base and Instruct versions Xiaomi MiMo-V2.5 - 310B Total Params / 15B Active Params - Base and Instruct versions MIT License Opensource AI just keeps getting better [ima…
🎉 MiMo-V2.5 series is here, day-0 support is now live in SGLang! Two models to try: 1️⃣ MiMo-V2.5-Pro: 1.02T/42B MoE, hybrid attention, up to 1M context 2️⃣ MiMo-V2.5: full multimodal (text, image, video, audio), 310B/15B MoE, 1M context We also have day 0 support for this model …
MiMo-V2.5 achieved Day-0 adaptation across multiple chip platforms on the first day of open source release. Huge thanks to our hardware ecosystem partners for helping make MiMo-V2.5 easier to deploy and run efficiently across more environments: @awscloud、@AMD [image]
...It wasn't an intern's joke MiMo 2.5 (not Pro): > Trained on a total of ~48T tokens using FP8 mixed precision. The context window supports up to 1M tokens. We've got another 1M class, and the largest disclosed pretrain. Congrats Xiaomi. [image]