GLM-OCR
Up to 30%GLM-OCR provides API OCR on LLM.API so scans and screenshots become structured, usable text.
What is GLM-OCR?
GLM-OCR is an OCR-oriented model on LLM.API (`glm-ocr`). OCR model by Zai. It takes document, image and produces text so document workflows can digitize content before LLM analysis.
Providers
LLM.API routes GLM-OCR to the providers below, with discounted effective rates versus list price.
List price by provider ($ / 1M tokens)
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $0.01; out — per page | 8K tokens | — |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.
Try this model
Test GLM-OCR right here — free to start.
Suggestions for your first prompt
Code snippet
Call GLM-OCR through the OpenAI-compatible API — OCR / document endpoint via LLM.API (see docs).
from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAP_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.chat.completions.create(
model="glm-ocr",
messages=[
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."},
],
)
print(resp.choices[0].message.content){
"model": "glm-ocr",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}5 Core Capabilities
Searchable archives
Turns paper archives into indexable text for search. Tuned to how teams typically call GLM-OCR.
Preprocessing for LLMs
Produces clean text that chat models can summarize or extract from. Tuned to how teams typically call GLM-OCR.
Multilingual scripts
Useful when documents mix languages or specialized glyphs. Tuned to how teams typically call GLM-OCR.
Document text extraction
Reads printed and scanned pages into machine-usable text.
Layout awareness
Handles multi-column and mixed layout documents better than naive OCR. Tuned to how teams typically call GLM-OCR.
6 Most Valuable Use Cases
- Historical archive indexing projects with GLM-OCR
- Preprocessing scans before LLM extraction
- Scanned PDF to searchable text pipelines with GLM-OCR
- Invoice and receipt digitization
- Warehouse label and packing-slip reading with GLM-OCR
- Screenshot-to-text for support tooling
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent AI Routing
Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model. -
Cost-Aware Execution
Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.
More performance, less spend. -
Resilient Fallback Flows
Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.
Never go dark on users. -
Deep LLM Observability
Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.
See every token, everywhere. -
Task-Level Orchestration
Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.
Think tasks, not models. -
High-Throughput Batch APIs
Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.
Scale from 1 to millions.
Why run GLM-OCR on LLM.API?
Unified AI Routing
Practical: Reach GLM-OCR and sibling models through one OpenAI-compatible endpoint.
Cost Control
Production: Compare provider price points and keep spend visible as you scale GLM-OCR.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Observability
Trace prompts, tokens, and errors for GLM-OCR alongside the rest of your stack.
Drop-in SDKs
Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Swap GLM-OCR for chat, media, or embedding alternatives without rewriting auth.
When to Use — When NOT to Use
Use it if...
- Your workflow starts with scans, photos, or screenshots (GLM-OCR)
- You want OCR output that downstream LLMs can analyze (GLM-OCR)
- You must turn images or PDFs into text at API scale (GLM-OCR)
Avoid if...
- Your documents are already clean digital text
- You require human-certified legal transcription without review
- You need creative image generation rather than text extraction
COMMUNITY
What developers say about OCR models
Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to GLM-OCR.
- Document-intelligence reviews score Mistral OCR around 4.3/5 for extraction accuracy, structured output and broad language coverage.
- Practitioners highlight bounding boxes, confidence scores and JSON-schema output as the features that make it pipeline-ready.
- It is framed as a developer/enterprise tool rather than an end-user document app.
SOURCES
Frequently Asked Questions
Which providers serve GLM-OCR?
LLM.API currently lists: zai. Availability can vary by region and account.
Where is the canonical page for GLM-OCR?
https://llmapi.ai/models/zhipu-glm-ocr/
What are limitations of GLM-OCR?
Like other API models, GLM-OCR can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
Is GLM-OCR a chat model?
No—GLM-OCR is categorized as a ocr model. Use the matching API surface rather than assuming chat completions.
Can I use tools or structured outputs with GLM-OCR?
Tooling support varies; for pure ocr models, prefer the modalities listed rather than assuming chat tools.
Does GLM-OCR understand documents semantically?
GLM-OCR focuses on text extraction. For deep semantic analysis, pass OCR output into a chat model on LLM.API.
What is the context length for GLM-OCR?
Reported context for GLM-OCR is 8K tokens. Always verify the active provider row if multiple providers are listed.
Does GLM-OCR support streaming?
Streaming depends on the active provider; check the providers table on this page for flags.
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