GLM-OCR Technical Report

arXiv cs.CL / 3/12/2026

📰 NewsTools & Practical UsageModels & Research

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Key Points

GLM-OCR is an efficient 0.9B-parameter compact multimodal model designed for real-world document understanding, combining a 0.4B-parameter CogViT visual encoder with a 0.5B-parameter GLM language decoder.
To address inefficiency of standard autoregressive decoding in deterministic OCR tasks, GLM-OCR introduces a Multi-Token Prediction (MTP) mechanism that predicts multiple tokens per step, improving decoding throughput while keeping memory overhead low through shared parameters.
System-level, a two-stage pipeline is adopted: PP-DocLayout-V3 first performs layout analysis, followed by parallel region-level recognition.
Extensive evaluations on public benchmarks and industrial scenarios show GLM-OCR achieves competitive or state-of-the-art performance in document parsing, text and formula transcription, table structure recovery, and key information extraction.
Its compact architecture and structured generation make it suitable for both resource-constrained edge deployment and large-scale production systems.

Abstract

GLM-OCR is an efficient 0.9B-parameter compact multimodal model designed for real-world document understanding. It combines a 0.4B-parameter CogViT visual encoder with a 0.5B-parameter GLM language decoder, achieving a strong balance between computational efficiency and recognition performance. 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. At the system level, a two-stage pipeline is adopted: PP-DocLayout-V3 first performs layout analysis, followed by parallel region-level recognition. Extensive evaluations on public benchmarks and industrial scenarios show that GLM-OCR achieves competitive or state-of-the-art performance in document parsing, text and formula transcription, table structure recovery, and key information extraction. Its compact architecture and structured generation make it suitable for both resource-constrained edge deployment and large-scale production systems.