Zhipu AI

GLM-OCR

About

GLM-OCR is a 0.9B open-weight model from Zhipu AI. Compact multilingual document OCR and markdown extraction. It accepts text, images, and documents. Context window is 8K. License is MIT. Released January 30, 2026.

Parameters
0.9B
Published parameter count.
Context window
8K
Tokens of context on a request.
License
MIT
License on the weights.

Pricing

Token rates are US dollars per 1M tokens. Image rates are per 1K images. Audio rates are per 1M audio seconds.

Rate	Price
Input / 1M	$0.10
Cached input / 1M	$0.02
Output / 1M	$0.20

Compare

GLM-OCR is the smallest of these document models, at 0.9B, and its published input rate is the lowest, $0.10 / 1M input.

Model	What it does	Price	Parameters	Context
GLM-OCR	Compact multilingual document OCR and markdown extraction.	$0.10 / 1M input	0.9B	8K
DeepSeek-OCR-2	DeepSeek OCR 2 and markdown extraction.	$0.25 / 1M input	3.4B	32K
dots.mocr	Multilingual document layout parsing and markdown OCR.	$0.20 / 1M input	3B	32K
Unlimited-OCR	Long-horizon document parsing with batched multi-page OCR.	$0.25 / 1M input	3B	32K

Benchmarks

Published scores for GLM-OCR, from the OmniDocBench leaderboard.

Evals

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	OmniDocBench v1.6: 95.22.
Cost and performance	Lower listed rate, mid-pack latency

Methods

Methods this model serves. Payload shapes are in the docs.

Method	Returns
ocr	Lines of text
markdown	The page as Markdown

Estimate cost

Estimate. A page is 2,500 input tokens and 1,000 output tokens.

Pages

$0.45

Quick start

Call this model on the OpenAI-compatible gateway. The model id is already filled in.

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="zai-org/glm-ocr",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

print(response.choices[0].message.content)