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GPT-5.6 Sol (high) vs Grok 4.5

Should you write with GPT-5.6 Sol (high) or Grok 4.5? 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 (high) leads overall, 87.6 to 81.9. Heads-up: GPT-5.6 Sol (high) 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).

GPT-5.6 Sol (high)
#33 Elo 2134 · 87.6/100
Grok 4.5
#76 Elo 1618 · 81.9/100
Cost / script
$0.155 vs $0.063
Human baseline
90.9 GPT-5.6 Sol (high) falls below it · Grok 4.5 falls below it

The verdict

GPT-5.6 Sol (high), comfortably. It's #33 against #76, close to 6 points of overall score apart, and Grok 4.5 doesn't beat it anywhere. The big misses for Grok 4.5 are Length Adherence, a sixteen-point gap, and visual cues, nine points behind. On hook and voice the gap is smaller but still there. Grok 4.5's case is price and speed: it's about two and a half times cheaper at $0.063 per script against $0.155, and it's the quicker of the two. Both are closed, so no open-weights argument either way. If you need lots of fast scripts and will fix length yourself, fine. Otherwise, the extra nine cents is worth it.

Pick GPT-5.6 Sol (high) for scripts that land on length with better cues, hook and voice.
Pick Grok 4.5 for faster, cheaper scripts when you'll handle length in the edit.

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 (high), orange is Grok 4.5, 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 (high) ahead: it leads there by 16.1 points. If you care about one thing, say voice or length, go straight to that row.

Tone & Voice Match19% weight
86.5
82.3
Writing Craft & Clarity13% weight
88.1
83.4
Substance, Accuracy & Value15% weight
88.2
84.5
Continuity & Emotion14% weight
86.1
80.1
YouTube Best Practices12% weight
86.8
82.2
Hook Strength10% weight
86.6
81.2
Length Adherence8% weight
89.3
73.2
Slop Score (EQ-Bench + ours)5% weight
92.8
88.8
Visual Cue Quality4% weight
89.4
80.3

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 (high) costs about 2.5x as much per script as Grok 4.5.

GPT-5.6 Sol (high)Grok 4.5
Overall / 10087.681.9
Writing Elo21341618
Score spread across drafts (± overall std)1.3302.950
Cost per script (USD)0.1550.063
Avg time per script (min)1.40.9
Open weightsNoNo

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 (high) · Grok 4.5. The whole scoring pipeline is on the methodology page.

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