GPT-5.6 Sol (ultra)
OpenAI · proprietary · writing benchmark
The short version
GPT-5.6 Sol (ultra) lands in the upper third of ToneBench at #25 of 167, with a writing Elo of 2187 and an overall score of 88.0 out of 100. Those numbers come from it writing its own version of the scripts behind 10 of my What's AI videos, five times each, with every draft scored blind against mine (how the scoring works).
For scale, my own scripts score 90.9 through the same judges and rubric. That's the real bar here, not a literal 100. It sits 2.9 points under it.
Its Elo comes from comparing its average on each script with every other model's average on the same script, with gaps inside the run-to-run noise counted as draws. The 95% range is 2147 to 2223. That range overlaps 23 other configurations, ranked #15 to #38, so its exact spot inside that band is too close to call on this board. What the ranges can and can't tell you. One disclosure: the GPT-5.6 Sol line also holds one of the three judge seats. The other two judges come from other model families, so no family scores itself alone (how the panel works).
Its biggest edge is Length: 91.9, 11th on the board and about 21 points above the board average. Even its weakest, Hook (86.8), sits above the board average, so there's no real weak spot to plan around.
It held steady across all 10 scripts, with its best on the personal roadmap / opinion (89.2).
It's also unusually consistent: its overall moves only ± 1.2 between runs of the same script, against ± 2.7 for the typical model. What you get on the first try is close to what you get every time.
It costs about $0.363 per script. GLM-5.3 Flash scores higher and costs about $0.007 per script, so on value this isn't the pick.
It's the best OpenAI writer on the board, ahead of the other 43 OpenAI configs. If you're staying with OpenAI, start here.
Among proprietary models, it's 22nd of 114. The best open-weights config, GLM-5.3 at #15, outranks it, so it's worth a look if you'd rather run open weights.
GPT-5.6 Sol is also on the board at other effort settings, and none of them beat this one; the closest is GPT-5.6 Sol (xhigh), at Elo 2165. The thinking-levels page shows what each setting costs and how long it takes, so you can see how much quality a cheaper or faster one gives up.
Skill profile
Read the shape: the further a corner reaches, the stronger the model is on that metric. The dashed line is the board average and the dotted line is the board's best score on each metric, so a corner outside the dashed line beats the average there. Hover a point for the exact numbers.
Metric by metric
Nine writing metrics, each scored 0–100 and blended into the overall by the editorial weight shown next to it. Rank is out of all 167 current-ranked models. The thick bar is this model; the thin line above it is the field's best on that metric (darker) and the one below is the field's average (lighter), on the same scale. The small ± is how much the score moves from run to run, and the ? next to each name says what the metric rewards.
Script by script
The same 10 real scripts every current-ranked model writes, each scored on its own as the mean of 5 runs. Different formats stress different skills: a model can nail a tight explainer and still stumble on a personal story, so look for the format closest to what you write.
Consistency: how much it moves between runs
A model whose drafts swing from run to run is harder to use than its average suggests. Every score on this page is the mean of 5 runs per script, and the ± is the run-to-run standard deviation.
Its steadiest is Cues: ± 0.8 run to run, when the typical model swings ± 3.2.
| Metric | This model | Board median | Verdict |
|---|---|---|---|
| Tone ? | 86.2 ± 1.2 | ± 1.7 | typical |
| Craft ? | 88.1 ± 1.0 | ± 1.5 | typical |
| Substance ? | 89.0 ± 1.1 | ± 2.0 | steadier than most |
| Flow ? | 86.0 ± 1.3 | ± 1.8 | typical |
| YouTube ? | 87.7 ± 1.5 | ± 2.9 | steadier than most |
| Hook ? | 86.8 ± 1.5 | ± 2.0 | typical |
| Length ? | 91.9 ± 5.1 | ± 8.6 | steadier than most |
| Slop ? | 93.4 ± 0.9 | ± 2.1 | steadier than most |
| Cues ? | 90.1 ± 0.8 | ± 3.2 | steadier than most |
Cost, time and tokens
Logged on every run. None of it touches the writing score, but it's often what decides whether a model fits your pipeline at all. Time is the average time to get one accepted script, retries included, and cost is priced per script at list price.
What each judge scored it
One judge can have taste of its own, which is why there are three, from three model families, each scoring the same 50 stored drafts blind with the identical rubric. The overall of 88.0 combines their scores with the parts computed in code (length, and half of the slop score). The highest and lowest judge are 7.7 points apart on its overall. That's a typical amount of judge disagreement for this board. How the panel works.
How we ran GPT-5.6 Sol (ultra)
- Accessed viaCodex CLI (OpenAI subscription) · model page ↗
- Exact model / route id
gpt-5.6-solatreasoning_effort: ultra - Generations5 independent runs × 10 scripts = 50 drafts, stored immutably after generation
- Run dates2026-09-03
- Judges3-judge panel: Claude Opus 5, high effort (Anthropic) + DeepSeek V4.1 Flash (OpenRouter) + GPT-5.6 Sol, medium reasoning (Codex CLI). All three score every draft independently with the identical rubric, blind to which model wrote it, and each one has to quote evidence before it scores a metric. Seven metrics are the plain average of the three judges; Length is computed from the spoken word count, and Slop is half computed (the EQ-Bench slop-score pipeline plus our banned-phrase scan) and half judged. The Elo is cross-checked with a Bradley-Terry refit (methodology)
- Sampling and promptprovider-default sampling, no fine-tuning, no system-prompt changes. Every current-ranked model gets the same prompt, about 11,000 tokens (the count shifts a bit with each model's tokenizer): a style guide built from my voice and storytelling guides, the target length, and the brief with its research packet. What changes from row to row is the model configuration
- What stays privatethe exact rubric and any non-public reference material. Scripts are normally benchmarked before their video goes out, which lowers the odds a model has seen mine, though it doesn't make that impossible (why)
Frequently asked questions
How good is GPT-5.6 Sol (ultra) at writing?
It's #25 of 167 on ToneBench, with a writing Elo of 2187 and an overall score of 88.0/100. It wrote its own version of the scripts behind 10 of my What's AI videos 5 times each, and three judges from three model families scored every draft blind against mine. Length and half of the slop score are computed in code.
Is GPT-5.6 Sol (ultra) the best OpenAI model for writing?
Yes. Of the 44 OpenAI configs on the board, GPT-5.6 Sol (ultra) writes best.
Is GPT-5.6 Sol (ultra) good value for the money?
It costs about $0.363 per script. GLM-5.3 Flash scores higher and costs less, so it isn't the value pick.
What are GPT-5.6 Sol (ultra)'s strengths and weaknesses?
Against the board average, its biggest edge is Length (91.9, +21.1) and even its weakest, Hook (86.8, +5.2), is above average. The nine-metric breakdown on this page shows the board average and board best for each.
How was GPT-5.6 Sol (ultra) evaluated?
Through Codex CLI (OpenAI subscription), using the exact model/route id gpt-5.6-sol at reasoning_effort ultra, first run on 2026-09-03. Each script ran 5 times with provider-default sampling and no fine-tuning. The 'How we ran it' section and the methodology page have the rest.
Is GPT-5.6 Sol (ultra) open source?
No, it's a proprietary (closed-weights) model. Among proprietary models it's 22nd of 114 for writing.