Tencent releases Youtu-Parsing-Omni, a 5B omni-modal parsing model scoring 96.96 overall on OmniDocBench v1.6
One model that parses documents, images, charts, geometry figures, audio and video into structured JSON.
// Key points
- 5B parameters in BF16. One model returns layout, text, tables, formulas, bounding boxes, timestamps, speech transcripts and OCR, and can also write captions, narratives and reports; the task prompt picks the output type.
- It scores 96.96 overall on OmniDocBench v1.6, the highest listed; on OmniParsingBench it averages 75.08, which the card calls the best open-weight result, second only to Gemini-3-Pro at 77.44.
- It runs on vLLM (with a plugin) and Transformers, which need different transformers versions (5.2.0 and 5.10.2) and so separate environments; audio and video need ffmpeg. The license is a custom youtu-parsing license.
Builder's takeFile understanding in my AI Cloud Drive runs as separate pipelines per format, so one 5B model that takes PDFs, screenshots, audio and video is worth a comparison on real user files, tested alongside LightOnOCR-3 from 10-09. It has a custom license, so confirm commercial use first.
// Background · from #Open models
Full timeline →- Oct 12 Qwen releases Qwen-Image-2.1-Turbo weights: a 7B image model that generates in 8 steps, under a research license
- Oct 12 Amazon releases Strands decider models in 2B, 12B and 26B-A4B sizes under Apache 2.0
- Oct 11 Cloudflare open-sources Clef-omni, a multimodal decision model, and cuts Clef-flash input from $0.09 to $0.038 per million tokens