Benchmark Almanac

LongText-Bench

Fading

Image models render long text in images. The benchmark reports separate English and Chinese scores.

GLM-Image reports English and Chinese scores in its official model card and launch post.

Made by
Z.ai
Released
January 2026
Kind
Image generation and editing
Level
Everyday
Scored
Checked automatically
Answers
Open-ended
Last in a launch post
January 2026, dropped before it saturated
Last model added
January 2026
1
Model scored in lab cards
1
Launch post lists it, from 1 lab
1
System card reports it, from 1 lab

GLM-Image leads with 0.97

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

  1. 1 GLM-Image 0.97

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 GLM-Image 0.97 Jan 2026 Lab model card

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
0.97 2026 0.97 2026

Source: Labs’ model cards

Labs headline it less often now, last in January 2026

Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.

Z.ai
DateLaunch postScoreAs the post writes it
Jan 14, 2026 GLM-Image 0.966 LongText-Bench EN 0.966, ZH 0.979

One system card reports it

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
Jan 14, 2026 GLM-Image model card 0.966 LongText-Bench EN 0.966, ZH 0.979 Capabilities

More in Vision

All 281 in Vision
  • MMMU In use 11.5K college-level multimodal questions across 30 subjects. Multiple-choice and open accuracy. GPT-5.1 85.4%
  • MMMU-Pro In use Harder MMMU: college-level multimodal questions with more options and vision-only inputs to remove text shortcuts. GPT-5.6 Sol 84.6%
  • CharXiv In use Chart understanding on real arXiv figures. Reasoning questions require synthesizing information from complex charts. Claude Fable 5 93.5%
  • MATH-Vision (MathVision) In use 3,040 competition math problems with visual context. Answer accuracy. Qwen 3.8 Max 97.7%
  • MMStar In use Models answer 1,500 human-curated image questions that cannot be solved without the image, across 6 core capabilities. Scored by accuracy. Qwen 3.8 Max 85.9%
  • ChartQA In use Models answer human-written and generated questions about chart images that need visual and arithmetic reasoning. Scored by relaxed accuracy with a 5 percent numeric tolerance. Command A Vision 90.9%