GLM-OCR vs Qwen2.5 VL 7B Instruct
Compare GLM-OCR and Qwen2.5 VL 7B Instruct side-by-side. See how these vision models stack up in OCR.
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GLM-OCR vs Qwen2.5 VL 7B Instruct Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | GLM-OCR | Qwen2.5 VL 7B Instruct |
|---|---|---|
| Organization | Z.ai | Qwen |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Jan 2025 |
| Context Window | — | 33K |
| Parameters | 0.9B | 7B |
| License | MIT | Apache 2.0 |
| Vision Tasks | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Captioning | Demo | |
| Chart Question Answering | ||
| Document Question Answering | ||
| Object Detection | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
GLM-OCR vs Qwen2.5 VL 7B Instruct: Overview
GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder-decoder architecture by Zhipu AI. The model combines a 0.4B-parameter CogViT visual encoder pre-trained on large-scale image-text data, a lightweight cross-modal connector with efficient token downsampling, and a 0.5B-parameter GLM language decoder, totaling 0.9B parameters. To address the inefficiency of standard autoregressive decoding in deterministic OCR tasks, GLM-OCR introduces a Multi-Token Prediction (MTP) mechanism that predicts multiple tokens per step, significantly improving decoding throughput while keeping memory overhead low through shared parameters. Training proceeds through four stages: visual encoder pretraining with MIM, CLIP, and distillation objectives; vision-language pretraining on document parsing, grounding, and VQA data; supervised fine-tuning on curated OCR datasets covering text, formula, table, and key information extraction; and full-task reinforcement learning to improve accuracy and structural consistency.
At the system level, GLM-OCR adopts a two-stage pipeline in which PP-DocLayout-V3 first performs layout analysis, followed by parallel region-level recognition. This design enables robust handling of diverse document layouts including tables, formulas, and multi-column text. The model supports document parsing and targeted recognition tasks, producing structured outputs in Markdown, JSON, and LaTeX formats across more than 100 languages. On the OmniDocBench V1.5 benchmark, GLM-OCR scores 94.62, and achieves 94.0 on OCRBench and 96.5 on UniMERNet for formula recognition.
Qwen2.5-VL-7B-Instruct is a 7-billion parameter vision-language model from Alibaba’s QwenLM team, released on January 26, 2025 under the Apache 2.0 license. It is the instruction-tuned variant of the 7B scale in the Qwen2.5-VL family, designed to process multimodal inputs such as text, images, charts, documents, and video. The model enables structured outputs—including JSON for structured content and bounding boxes for visual localization. Weights are publicly available on Hugging Face and GitHub, making it suitable for both research and applied multimodal use.