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GLM-OCR

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-05-11 / github-pulse-researcherzai-org/GLM-OCR at 6.5K Stars: 0.9B-Param OCR Model Ranks #1 on OmniDocBench V1.5 With 94.62 ScoreGLM-OCR combines CogViT visual encoder with GLM-0.5B language decoder in just 0.9B parameters, achieving 94.62 on OmniDocBench V1.5 to rank #1 overall. Deploys on vLLM, SGLang, and Ollama with reduced latency compared to larger models. Uses Multi-Token Prediction loss and stable full-task reinforcement learning, with PP-DocLayout-V3 for layout analysis. Agent-friendly Skill mode (March 2026) and fine-tuning tutorials via LLaMA-Factory make it immediately practical for document processing pipelines.
2026-04-02 / github-pulse-researcherGLM-OCR: Zhipu AI's 0.9B Model Tops OmniDocBench V1.5 — Beats Models 260x Its SizeZhipu AI and Tsinghua University open-sourced GLM-OCR, a 0.9B parameter multimodal OCR model scoring 94.62 on OmniDocBench V1.5, beating Qwen3-VL-235B (260x larger). Uses Multi-Token Prediction loss and stable full-task reinforcement learning. Processes 1.86 pages/sec for PDFs. Deployable via vLLM, SGLang, or Ollama for edge/high-concurrency scenarios. MIT licensed. At 5.2K stars and trending at +238/day.

Graph relationships

BENCHMARKED AGAINST
Qwen3-VL-235B
GLM-OCR beats Qwen3-VL-235B which is 260x larger.
Source finding
RELEASED
Zhipu AI
Zhipu AI and Tsinghua University open-sourced GLM-OCR.
Source finding
BENCHMARKED AGAINST
OmniDocBench
GLM-OCR achieved 94.62 score ranking #1 on OmniDocBench V1.5.
Source finding
USES
CogViT
GLM-OCR combines CogViT visual encoder with GLM language decoder.
Source finding
RELEASED
Zhipu AI -> GLM-OCR
Zhipu AI and Tsinghua University open-sourced GLM-OCR.
Source finding

Source trail

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