Slide generation human evaluation (Z.ai)
People compare HTML slide decks from two models on content, layout and visual quality. Metric is overall win rate.
In-house Z.ai evaluation. GLM-5 is compared with GLM-4.5.
- Made by
- Zhipu AI (Z.ai)
- Released
- February 2026
- Kind
- Taste and style
- Level
- Everyday
- Scored
- Graded by people
- Answers
- Open-ended
- Tasks
- Kept private
- Strongest model tested
- GLM-5 #88 on the capability index
- Last in a system card
- February 2026
- Last model added
- February 2026
GLM-5 leads with 67.5%
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 GLM-5 67.5%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GLM-5 | 67.5% | Feb 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.
Source: Labs’ model cards
Labs report it only in system cards
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.
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.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Feb 17, 2026 | GLM-5 technical report | 67.5 | Slides human evaluation, overall win rate vs GLM-4.5 (Section 4.2, text) | Capabilities |
More in Writing and taste
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- WritingBench Fading Models complete 1,000 real-world writing requests across 6 domains and 100 subdomains. A judge model scores each reply on five query-specific criteria. GPT-5 83.9
- Creative Writing (MiniMax in-house) Only in system cards Models write creative pieces for real Hailuo AI user requests. Automatic assessments score the writing.
MiniMax-Text-01 81.3% - DeepSeek Chinese functional writing evaluation Only in system cards Models answer 3,170 everyday Chinese writing prompts such as reports, emails and speeches. Pairwise judging against a baseline model gives a win rate.
DeepSeek-V4-Pro 62.6% - Task-dependent output homogenization evaluation Only in system cards Models answer prompts several times. A judge scores functional diversity where it is wanted and consistency where one answer is right.
Muse Spark 0.21 - DeepSeek Chinese creative writing evaluation Only in system cards Models write Chinese fiction, prose, poetry and lyrics for 2,837 prompts. Pairwise judging gives separate win rates for instruction following and writing quality.