GLM Image: Readable Text on Images (Benchmarks, Architecture, and When to Use It)

If you ship posters, thumbnails, slide covers, or OG images, you already know the failure mode:

• the image looks fine
• the words look "almost right" (which is worse than wrong)

GLM Image (spelled GLM-Image in the release) is worth attention because it is explicitly optimized for text inside images.

Primary references:

• GLM Image project page
• GLM Image benchmarks/overview

TL;DR

• Use GLM Image when the image must contain readable words (posters, infographics, UI mockups, slides).
• Don't pick it if your main requirement is photorealistic portraits / identity consistency.
• For brand-critical assets: generate the background, but render the text in code (SVG/HTML/canvas) so the headline is deterministic.

Related reading on this site:

• AI topic hub
• AI orchestration for marketing: systems, not prompts
• ChatGPT prompts for SEO marketing (with quality gates)
• Internal linking strategy (topic cluster blueprint)

What's under the hood (why the architecture matters)

GLM-Image is described as a hybrid setup that combines:

• an auto-regressive component (reported as ~9B parameters) to understand instructions and plan composition
• a diffusion decoder (reported as ~7B parameters) to add detail and texture

The idea is to get the best of both worlds:

• diffusion models can draw, but often struggle with long, structured instructions
• auto-regressive models can follow instructions, but historically lagged in pure image quality

For text-on-image, the key detail is the dedicated glyph/text pathway (described as a glyph encoder that works at character level). That is exactly what you want when the output must contain real words, not "text-like noise".

Benchmarks (reported): the part that matters for posters

If you care about text inside images, do not over-index on generic image benchmarks. The useful signal is text-focused tests.

The project pages highlight:

• CVTG-2k (Complex Visual Text Generation): Word Accuracy 0.9116 (reported)
• LongText-Bench (long poster-style text): Chinese 0.9788, English 0.9524 (reported)

A simplified CVTG-2k table (reported):

Interpretation:

• if your deliverable is a poster with a headline, Word Accuracy can matter more than overall image quality
• GLM Image is positioned as a tool for communication graphics, not a general photorealism model

The practical system: generate backgrounds, render typography in code

There are two separate problems:

1. generate a good image
2. deliver exact text

If #2 is strict (brand name, product name, legal line, exact headline), the safest workflow is:

• generate a background (or style)
• render text deterministically on top

Why this wins:

• you control font, line breaks, sizes, and spacing
• you avoid "almost correct" text that platforms will happily cache

Quick comparison table (what to do when)

Prompt pattern for text-on-image (model-agnostic)

• specify language
• include the exact text in quotes
• ask for high contrast
• specify layout (top / center / bottom, margins)

Example:

Minimal poster. Dark background. High contrast.
Exact headline text (English), centered:
"GLM Image"
"Text rendering is the benchmark"
No extra letters. No logos. No watermark.

If you tell me your main use case (OG images for blog posts vs posters vs slide covers), I will give you:

• 3 prompt templates
• a simple typography system (sizes/spacing)
• and a fallback when the model produces almost-correct text