--- title: "Xiaomi's open MiMo-V2.6-Pro tops the open-weight chart — at 13 cents a task" date: 2026-10-04 category: Foundation Models site: NeuroAI canonical: https://neuroai.site/a/na-model-xiaomi-mimo-v26-open language: en --- # Xiaomi's open MiMo-V2.6-Pro tops the open-weight chart — at 13 cents a task > Xiaomi open-sourced MiMo-V2.6-Pro, a full-modal model it says leads open weights on Artificial Analysis, with a per-task cost near US$0.13. A flagship AI model usually means a flagship price. The companies that can't pay step aside. Xiaomi, a phone maker, just released a model that claims to match the frontrunners for a fraction of the cost. And it gave the weights away. ## The release: MiMo-V2.6 On September 22, 2026, Xiaomi (小米) released and open-sourced the MiMo-V2.6 series: a flagship MiMo-V2.6-Pro and an efficient MiMo-V2.6-Flash. Both natively accept text, image, video, and audio — what Xiaomi calls full-modal (全模态) input. Reported specs for the Pro: - **Over 1 trillion total parameters**, with about **42B active** (MoE-style sparse activation) - **1 million-token context** - Positioned as the only open-source full-modal Pro model in its tier Xiaomi says the family is released under a permissive license (the project site lists MIT for the MiMo line), and that it also open-sourced the reinforcement-learning know-how behind the models. ## The claim that matters: cost per task The headline is not just capability but economics. On Artificial Analysis's composite intelligence index, Xiaomi says MiMo-V2.6-Pro scored **46 points**, ranking first among open-weight models and first among domestic Chinese models — ahead of Kimi K3 and Qwen3.8 Max. More striking is the cost column. Xiaomi cites a measured **unit task cost of US$0.13** (about **HK$1.0**), which it claims is one-twentieth to one-sixtieth the cost of overseas models at similar intelligence. If even roughly true, it redraws the "smart-but-affordable" line for self-hosted and API users alike. ## What it's actually for MiMo-V2.6 is pitched at real work, not demos: - **Complex reasoning and long-horizon tasks** - **Multi-agent collaboration and large coding projects** - **Office, web, and frontend design; video editing; 3D and music generation** - **Computer control, cybersecurity, and research** The most interesting pilot is scientific. Xiaomi says MiMo-V2.6-Pro acted as a "Co-Scientist," helping its materials team design and screen metal-organic framework (MOF) candidates for adsorbing PFAS — the "forever chemicals" — in a real internal research loop. ## The bigger Xiaomi bet MiMo is not a side project. It powers Xiaomi's "human-car-home" (人车家) ecosystem: the XiaoAI assistant, HyperOS, and smart cockpits. Baidu Baike notes Xiaomi plans to invest at least **¥60 billion** in AI over three years — roughly **US$8.5B / HK$66B** — and that its on-device MiMo model passed national large-model filing in July 2026. That context explains the strategy: a phone-and-device company wants models that run on its own hardware and don't bleed margin to outside APIs. ## How it compares - **vs. closed frontiers:** Xiaomi claims parity on cost-adjusted intelligence, not raw score. - **vs. other open weights:** it leans on full-modal input as its differentiator, arguing most open Pro models are text-or-image only. - **vs. Xiaomi's own past:** the V2.6 generation keeps the same pretraining architecture but reportedly doubles intelligence via far larger RL compute. ## What the open-weight move means globally Xiaomi is not a typical model lab — it is a hardware company that ships billions of devices a year. Open-sourcing MiMo-V2.6 means the weights are free for anyone to download, fine-tune, and embed, not just license through a paid API. For global developers, that lowers the barrier to building on a Chinese full-modal model without sending data to a Chinese server. The cost framing is the real hook. If a frontier-level task genuinely costs around 13 cents through MiMo versus several dollars through a closed API, the economics of shipping AI features change for small teams and indie developers. The caveat is that "task cost" depends entirely on how you define a task; Xiaomi's number reflects its own measurement, and your mileage will vary with prompt style, context length, and retry rate. There is also a strategic read. A device maker that owns both the silicon-to-showroom pipeline and the model can bundle intelligence into hardware at near-zero marginal cost — a position pure software labs cannot easily copy, and one that explains why a phone company is willing to give away frontier-grade weights. ## Honest limitations - The Artificial Analysis "46 points" and the cost ratios are Xiaomi's framing; we did not reproduce them, and benchmark methodologies differ across rounds. - "Only open-source full-modal Pro model" is Xiaomi's claim; the field moves monthly, so verify before quoting it as permanent. - The PFAS/MOF "Co-Scientist" example is an internal demo, not a published, peer-reviewed result. - We did not benchmark latency, multimodal quality, or true per-task cost on our own workloads. ## What readers can do now - **Download and test:** pull MiMo-V2.6-Pro weights from Hugging Face and run the MiMo Desktop client or an OpenAI-compatible API endpoint. - **Stress the cost claim:** run your own representative tasks and measure tokens spent versus a closed model to see if the 13-cent figure holds. - **Watch the ecosystem:** Xiaomi's ¥60B / US$8.5B three-year AI commitment signals deeper on-device-model integration across phones, cars, and home devices. --- Published by NeuroAI (https://neuroai.site/) — https://neuroai.site/a/na-model-xiaomi-mimo-v26-open Free to quote with attribution and a link to the original.