PaddleOCR‑VL‑1.6, a compact 0.9B visual‑language model, diagnoses three types of weak regions, enriches targeted data, and applies a three‑stage CPT‑SFT‑RL training pipeline to reach a 96.33% overall score on OmniDocBench v1.6, surpassing much larger models across all document‑parsing tasks.
LongCat-Next, a 3‑billion‑parameter multimodal model released by Meituan, adopts a pure discrete token‑based architecture (DiNA) and next‑token prediction, outperforming same‑size rivals on OmniDocBench‑EN, CharXivRQ, and matching QwenVL on visual tasks, while avoiding catastrophic forgetting and achieving a SWE‑Bench score of 43.0, as demonstrated through extensive benchmarks, receipt extraction, OCR, audio dialect reasoning, and image generation experiments.
FireRed‑OCR‑2B, an open‑source 2‑billion‑parameter visual‑language model, addresses structural hallucination in document OCR through a geometry‑aware data factory and a three‑stage training pipeline, achieving a 92.94 OmniDocBench v1.5 score and leading end‑to‑end performance while remaining lightweight enough for consumer‑grade GPUs.
GLM-OCR, a 0.9B‑parameter multimodal OCR model from Zhipu, achieves the highest score (94.62) on OmniDocBench V1.5, offers lightweight deployment via vLLM, Ollama, API and SDK, and outperforms larger rivals like DeepSeek‑OCR and PaddleOCR in speed and accuracy.
PaddleOCR‑VL‑1.5, the latest Baidu release, uses only 0.9 B parameters to achieve 94.5% accuracy on OmniDocBench v1.5, surpassing larger open‑source and commercial OCR models, while offering multi‑task, multi‑language support, lightweight deployment, and detailed performance benchmarks.
DeepSeek-OCR 2 introduces Visual Causal Flow and a LLM‑based visual encoder, achieving 91.09% accuracy on OmniDocBench v1.5, while providing detailed installation, two inference modes (vLLM and Transformers), and an analysis of its strengths and limitations for complex document processing.
Deepseek-OCR leverages a visual‑compression approach, combining DeepEncoder and the DeepSeek3B‑MoE‑A570M decoder, to represent document text with far fewer visual tokens, achieving up to 97% OCR accuracy and surpassing GOT‑OCR2.0 and MinerU2.0 on OmniDocBench, while the article offers a one‑click deployment tutorial.
