Xiaomi Open-Sources 0.8B Parameter Document Understanding Model Xiaomi-OCR-0, Claims Leading Benchmark Performance
Xiaomi has released and open-sourced Xiaomi-OCR-0, a 0.8B parameter model unifying document parsing, key information extraction, and visual question answering, reporting top scores in several benchmarks.
Chinese technology company Xiaomi released and open-sourced its lightweight all-in-one document understanding model, Xiaomi-OCR-0, on the evening of October 30, 2026. With only 0.8 billion parameters, the model integrates three core capabilities: document parsing, Key Information Extraction (KIE), and OCR Visual Question Answering (VQA). Xiaomi states that the model is designed to tackle structured parsing challenges in complex tables, academic papers, and receipts, aiming to evolve the "Super Xiao Ai" assistant on Xiaomi phones from simple "text recognition" to deeper "document understanding."
According to Zhidongxi's report, Xiaomi-OCR-0 achieved a score of 96.83 on OmniDocBench v1.6, an authoritative benchmark for comprehensive document parsing, ranking first among models under 1B parameters. Its average score across five mainstream OCR VQA benchmarks was 83.2, outperforming models such as HunyuanOCR and TokenVL, and approaching the performance of Qwen3.5-4B with only approximately 20% of the parameter count. Additionally, on Real5-OmniDocBench, which simulates real-world physical capture conditions like curvature, reflections, and moiré patterns, the model scored 95.24, ranking first among compared models. It should be noted that these results are self-reported by the vendor and have not yet been independently reproduced.
Technically, Xiaomi-OCR-0 is built upon the Qwen3.5-0.8B-Base foundation and trained on approximately 170 million high-quality samples. The team employed a three-stage progressive strategy: first, Q-Mask text anchoring to strengthen spatial correspondence between visual features and text tokens; second, large-scale multi-task pre-training to establish a base capability for full-page parsing; and finally, mixed-task reinforcement learning to enhance complex layout restoration and multi-step reasoning. Regarding data engineering, Xiaomi introduced a heterogeneous expert consensus mechanism and a rendering-guided verification loop to filter single-model biases and correct structural defects.