{"slug": "xiaomi-details-ai-models-and-feature-portfolio", "title": "Xiaomi Details AI Models and Feature Portfolio", "summary": "Xiaomi has expanded its AI portfolio across language, reasoning, code, and voice technologies, entering the open-source LLM race in April 2025 with the 7 billion-parameter MiMo model. The company's MiMo-V2-Flash, a 309 billion-parameter MoE model released in December 2025, reportedly rivals top-tier models on software-engineering tests while achieving inference costs at 2.5% of Claude's. The portfolio also includes miclaw, a system-level autonomous agent, and OmniVoice, an open-source multilingual voice cloning model supporting over 600 languages.", "body_md": "# Xiaomi Details AI Models and Feature Portfolio\n\nGizmochina reports that Xiaomi has expanded its AI portfolio across language, reasoning, code, and voice technologies. The article says Xiaomi first entered the open-source LLM race in April 2025 with MiMo, a **7 billion**-parameter model; Gizmochina reports the reinforcement-learning variant scored **95.8%** on the MATH-500 benchmark and reportedly outperformed OpenAI's o1-mini and Alibaba's Qwen-32B-Preview on AIME 2024 and 2025. Gizmochina also reports the company released a larger MoE model, MiMo-V2-Flash, in December 2025, described as a **309 billion**-parameter model with only **15 billion** parameters active at inference, rivaling top-tier models on software-engineering tests while achieving **150 tokens per second** and an inference cost reportedly at **2.5%** of Claude's, with API input pricing reported at **$0.1 per million** input tokens. Gizmochina documents training corpus sizes, MIT licensing, Hugging Face availability, and credits Luo Fuli as development lead. The roundup also covers miclaw, a system-level autonomous agent built on MiMo-V2-Pro, and OmniVoice, an open-source multilingual voice cloning model supporting over 600 languages.\n\n### What happened\n\nGizmochina published a roundup documenting Xiaomi's full AI model portfolio as of June 2026. The article reports Xiaomi entered the open-source LLM race in April 2025 with **`MiMo`**, a 7 billion-parameter model released under an MIT license on Hugging Face. Gizmochina reports the reinforcement-learning variant of MiMo-7B scored **95.8%** on the MATH-500 benchmark, reportedly outperforming OpenAI's o1-mini and Alibaba's Qwen-32B-Preview on AIME 2024 and 2025. The article credits Luo Fuli as development lead and reports training figures of **200 billion** reasoning tokens across a **25 trillion**-token corpus.\n\n### MiMo-V2-Flash\n\nGizmochina reports that **`MiMo-V2-Flash`**, released in December 2025, is a sparse Mixture-of-Experts model with **309 billion** total parameters and roughly **15 billion** active at inference -- a design that keeps per-inference compute low while maintaining large model capacity. Gizmochina reports the model ranked among the top two open-source models on reasoning benchmarks, rivaled Claude Sonnet 4.5 on SWE-Bench Verified software-engineering tests, achieved around **150 tokens per second**, and priced API input at **$0.1 per million tokens**, described as roughly **2.5%** of Claude's API cost. Weights and code are available on GitHub and Hugging Face under MIT license; an arXiv technical report (2601.02780) covers the architecture in detail.\n\n**miclaw and OmniVoice:** The roundup also covers **`miclaw`**, a system-level autonomous AI assistant announced in March 2026 that embeds MiMo-V2-Pro into the device OS to control apps, navigate mobile browsers, and manage IoT devices. **`OmniVoice`**, open-sourced in May 2026, is a multilingual voice cloning model supporting over 600 languages with emotional transitions and regional dialect synthesis.\n\n### Benchmark verification\n\nThe cited performance and cost figures are Xiaomi-reported. Independent replication of the SWE-Bench Verified and AIME 2024/2025 results will be needed to confirm Xiaomi's claims.\n\n## Scoring Rationale\n\nSingle-source Gizmochina roundup summarising Xiaomi's LLM portfolio covering releases from April 2025 to May 2026. The underlying models were notable at release -- MiMo-V2-Flash's claimed cost-performance ratio rivaling Claude Sonnet 4.5 on SWE-Bench is significant if independently confirmed -- but this article is a retrospective explainer, not a new announcement. Solid-tier as a useful practitioner reference on an expanding open-weight model family.\n\nPractice interview problems based on real data\n\n1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with.\n\n[Try 250 free problems](/problems)", "url": "https://wpnews.pro/news/xiaomi-details-ai-models-and-feature-portfolio", "canonical_source": "https://letsdatascience.com/news/xiaomi-details-ai-models-and-feature-portfolio-190e2685", "published_at": "2026-06-12 04:59:08.102528+00:00", "updated_at": "2026-06-12 04:59:11.291752+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-products", "ai-agents", "natural-language-processing"], "entities": ["Xiaomi", "MiMo", "Gizmochina", "OpenAI", "Alibaba", "Hugging Face", "Luo Fuli", "OmniVoice"], "alternates": {"html": "https://wpnews.pro/news/xiaomi-details-ai-models-and-feature-portfolio", "markdown": "https://wpnews.pro/news/xiaomi-details-ai-models-and-feature-portfolio.md", "text": "https://wpnews.pro/news/xiaomi-details-ai-models-and-feature-portfolio.txt", "jsonld": "https://wpnews.pro/news/xiaomi-details-ai-models-and-feature-portfolio.jsonld"}}