GLM-OCR
by zai-org
#1 document OCR at 0.9B — MIT licensed, edge-deployable
zai-org/GLM-OCRmixpeek://image_extractor@v1/zai_glm_ocr_v1Overview
GLM-OCR is a tiny (0.9B parameter) multimodal OCR model built on the GLM-V encoder-decoder architecture. Despite its small size, it ranks #1 on OmniDocBench V1.5 (94.62 overall score), outperforming models 10x its size on complex document understanding tasks including tables, formulas, handwriting, and multi-column layouts.
Its MIT license and sub-1B parameter count make it ideal for edge deployment, serverless functions, and cost-sensitive pipelines. On Mixpeek, GLM-OCR powers document text extraction for PDFs, scanned images, and screenshots where high accuracy matters more than raw throughput.
Architecture
GLM-V encoder-decoder with vision encoder (ViT variant) and autoregressive text decoder. 0.9B total parameters. Processes document images at native resolution with adaptive tiling for multi-page documents.
Mixpeek SDK Integration
import { Mixpeek } from "mixpeek";
const mx = new Mixpeek({ apiKey: "API_KEY" });
// Managed: create a collection over a bucket; Mixpeek runs this model's extractor
const collection = await mx.collections.create({
namespace_id: "my-namespace",
collection_name: "my-collection",
source: { type: "bucket", bucket_ids: ["bkt_your_bucket"] },
feature_extractor: {
feature_extractor_name: "ocr",
version: "v1",
parameters: { model_id: "zai-org/GLM-OCR" },
},
});Capabilities
- #1 on OmniDocBench V1.5 (94.62 overall)
- Tables, formulas, handwriting, multi-column layout support
- Only 0.9B parameters — runs on edge devices and serverless
- MIT license for unrestricted commercial use
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| OmniDocBench V1.5 (overall) | Score | 94.62 | ZAI, 2026 — Model Card |
Performance
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Specification
Research Paper
GLM-OCR: A Compact Multimodal OCR Model
arxiv.orgBuild a pipeline with GLM-OCR
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