GPT-5.6 Sol (ultra) vs GLM-5
Should you write with GPT-5.6 Sol (ultra) or GLM-5? On this page the only thing that changes is the model. Both wrote their own version of the scripts behind 10 of my What's AI videos, five drafts per script, and three judges from three model families scored every draft blind, with my finished script as the reference. GPT-5.6 Sol (ultra) leads overall, 88.0 to 82.8. Heads-up: GPT-5.6 Sol (ultra) shares its model line with one of the three judges. The panel spans three model families, so no model here is scored by its own family alone (how the panel works).
The verdict
If you like GLM, this page is mostly a pointer to GLM-5.3. Against GPT-5.6 Sol (ultra), the older GLM-5 loses every metric, #66 against #25. The widest gaps are Length Adherence and visual cues, both more than ten points, so GLM-5 misses the target length by more and its production cues score much lower. The newer GLM-5.3 sits near the top of the board and keeps the open weights. GLM-5 on its own only makes sense when you're tied to that exact model and $0.050 per script against $0.363 matters more than quality.
Pick GPT-5.6 Sol (ultra) for any script you intend to publish.
Pick GLM-5 only when you're tied to that exact open-weight model and its low price per script is the priority.
How to read these numbers
Overall vs Elo. Overall is the weighted rubric score. Elo compares each model's five-draft average with every other model's, script by script, and counts gaps inside run-to-run noise as draws, so it rewards winning often and can disagree with overall. My own scripts score 90.9 on the same rubric, not a literal 100. Right now 2 of 167 ranked configurations reach it. A model above that line writes at that level on this rubric. That's not the same as beating my script line for line.
Reading the Elo intervals. Each 95% range comes from resampling every model's recorded run scores on each task and re-ranking. Comparing these separate ranges does not test the Elo gap between two models: far apart is strong evidence, and overlap isn't a tie. The tasks stay fixed, so the ranges say nothing about new tasks (the long version).
Metric by metric: where the gap comes from
Blue is GPT-5.6 Sol (ultra), orange is GLM-5, and the solid bar takes the metric. The weight under each name is how much it counts toward the overall. Weighted by how much each metric counts, Length Adherence does the most to put GPT-5.6 Sol (ultra) ahead: it leads there by 13.4 points. If you care about one thing, say voice or length, go straight to that row.
Cost, speed, and consistency
Quality is half the decision. The other half is what each script costs you, how long you wait, and how often you get a bad draft. The lead has a price: GPT-5.6 Sol (ultra) costs about 7.3x as much per script as GLM-5.
| GPT-5.6 Sol (ultra) | GLM-5 | |
|---|---|---|
| Overall / 100 | 88.0 | 82.8 |
| Writing Elo | 2187 | 1692 |
| Score spread across drafts (± overall std) | 1.230 | 2.400 |
| Cost per script (USD) | 0.363 | 0.050 |
| Avg time per script (min) | 3.3 | 3.1 |
| Open weights | No | Yes |
Green wins the row, and for spread, cost and time, lower wins. Cost is one script at recorded list prices, uncached and without judging, so your real bill will differ. Spread is how much the overall score moves across a model's drafts, and time is the average time to get one accepted script, retries included.
Want the script-by-script detail? Full scorecards: GPT-5.6 Sol (ultra) · GLM-5. The whole scoring pipeline is on the methodology page.
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