GPT-5.6 Sol (ultra) vs MiniMax M3
Should you write with GPT-5.6 Sol (ultra) or MiniMax M3? 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 82.7. 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
MiniMax M3 costs a small fraction of what GPT-5.6 Sol (ultra) does, and the scores show what you give up for it. GPT takes every metric, #25 against #71. MiniMax trails by close to eight points on both the Slop Score and YouTube Best Practices: more filler phrasing flagged, and weaker CTAs, open loops and re-engagement beats. MiniMax is open weights at around $0.023 per script against $0.363, which is a real difference if you're generating thousands of scripts. For a script you'll actually record, I'd pay the extra and skip the cleanup.
Pick GPT-5.6 Sol (ultra) for scripts you'll record and publish.
Pick MiniMax M3 for high-volume, open-weight drafting where a low price per script beats polish.
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 MiniMax M3, 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, Substance, Accuracy & Value does the most to put GPT-5.6 Sol (ultra) ahead: it leads there by 7.6 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 16x as much per script as MiniMax M3.
| GPT-5.6 Sol (ultra) | MiniMax M3 | |
|---|---|---|
| Overall / 100 | 88.0 | 82.7 |
| Writing Elo | 2187 | 1672 |
| Score spread across drafts (± overall std) | 1.230 | 1.980 |
| Cost per script (USD) | 0.363 | 0.023 |
| Avg time per script (min) | 3.3 | 1.8 |
| 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) · MiniMax M3. The whole scoring pipeline is on the methodology page.
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