Model Comparison
Too close to call: the best configurations, GLM-5.3 (max) and GPT-5.6 Sol (low), are 2 Elo apart, and their 95% ranges overlap. Among 78 models, each ranked by its best configuration, GLM-5.3 is #7 and GPT-5.6 Sol is #8. Cost per script: $0.240 for GLM-5.3 (max) and $0.113 for GPT-5.6 Sol (low).
Last updated , from board data generated at 01:13 UTC.
PickGLM-5.3
GLM-5.3 (max)
Pick GLM-5.3 (max) when the voice has to come from the model, with the highest tone score of the three settings at $0.240 a script.
PickGPT-5.6 Sol
GPT-5.6 Sol (low)
Pick GPT-5.6 Sol (low) for substance-heavy drafts at $0.113 and about a minute, since ultra costs 3.2 times as much and lands 5 Elo lower.
The board can't call this one. GLM-5.3 is the #7 model of 78 and GPT-5.6 Sol the #8 model, each ranked by its best setting, and GLM-5.3 (max) sits 2 Elo above GPT-5.6 Sol (low) with 95% ranges that overlap almost entirely. Low is Sol's best setting, so I'd skip ultra. GPT-5.6 Sol (ultra) lands 5 Elo under low, inside the same overlap, for about 3.2 times the cost and more than four times the wait. The scores show two different writers. GLM sounds a little more like me, one to two points up on tone, and it holds the requested length better. GPT-5.6 Sol brings more substance, 3.9 to 4.8 points more depending on the setting. Low costs $0.113, less than half of GLM's $0.240, and takes about a minute against six. With the writing this close, that makes GPT-5.6 Sol at low the one I'd buy for most drafts. A voice that's slightly off is harder for me to fix in an edit than a missing detail, so I'd keep GLM for the script where the voice has to arrive right.
Disclosure: GLM-5.3 and GPT-5.6 Sol each come from the same lab as one of the judges (GLM 5.3 Flash and GPT-6.1 Sol). Neither is scored by its own lab alone (how the panel works). The judge analysis measures how much each judge favors its own lab.
Writing Score and Writing Elo. The Writing Score (0 to 100) is the weighted average of the nine metric scores: seven from the judges, Length Adherence from code and the Slop Score half from each. The Writing Elo is a rating from pairwise comparisons: on each script, each configuration's average across its drafts is compared with every other configuration's, and gaps within the draft-to-draft variation count as draws. Elo rewards winning often, so the two can put configurations in a different order.
Elo ranges. Each 95% range shows how far a configuration's Elo could move if the drafts had come out slightly differently. It comes from resampling every configuration's recorded draft scores on each reference script and ranking again. Comparing two separate ranges does not test the Elo gap between them: ranges far apart are strong evidence for the order, and overlapping ranges do not mean a tie. The reference scripts stay fixed, so the ranges say nothing about new scripts. The methodology page has the full description.
Every ranked configuration of GLM-5.3 and GPT-5.6 Sol, scored on the same 10 scripts by the same four judges.
Shaded rows are the configurations chosen for the metric comparison below.
| Configuration | Writing Elo | 95% Range | Writing Score | Cost / Script | Time / Script | Reasoning Tokens | Configuration Rank |
|---|---|---|---|---|---|---|---|
| GLM-5.3 (max) | 2220 | 2183 to 2259 | 82.4 | $0.240 | 6.1 min | 46,444 | #15 |
| GPT-5.6 Sol (low) | 2218 | 2181 to 2251 | 82.3 | $0.113 | 0.9 min | N/A | #16 |
| GPT-5.6 Sol (ultra) | 2213 | 2173 to 2266 | 82.3 | $0.367 | 4.1 min | N/A | #18 |
Cost is one script at the API list price of the route used, uncached and without judging. Time is the average for one accepted script, retries included. N/A under Reasoning Tokens means the board has no separate reasoning count for that route.
Each finding is computed from the configurations above. Where two 95% ranges overlap, the board cannot order those two configurations.
Pick one configuration of each model to compare their metric scores, consistency across drafts and per-script scores. Each menu starts on the model's best configuration.
GLM-5.3 (max) scores higher on four of the nine metrics and GPT-5.6 Sol (low) on five. Weighted by each metric's share of the Writing Score, Length Adherence does the most to put GLM-5.3 (max) ahead, with a 4.6-point lead on that metric. The weight beside each metric is its share of the Writing Score.
| GLM-5.3 (max) | GPT-5.6 Sol (low) | |
|---|---|---|
| Score spread across drafts (±) | 1.39 | 1.27 |
| Lowest script score | 80.5 Script 10 | 79.8 Script 4 |
| Highest script score | 84.1 Script 7 | 84.2 Script 9 |
| Range across scripts | 3.6 | 4.4 |
Spread is how much the Writing Score moves between a configuration's drafts, and lower is steadier. Green marks the better number in each row.
Writing Score on each reference script. Gap is GLM-5.3 (max) minus GPT-5.6 Sol (low), and the higher score is in bold.
| Script | GLM-5.3 (max) | GPT-5.6 Sol (low) | Gap |
|---|---|---|---|
| Script 1 opinion / warning explainer | 82.1 | 82.1 | 0.0 |
| Script 2 news-analysis / skeptical explainer | 82.5 | 82.5 | 0.0 |
| Script 3 personal roadmap / opinion | 81.5 | 82.3 | -0.8 |
| Script 4 short explainer | 81.0 | 79.8 | +1.2 |
| Script 5 news-analysis / opinion explainer | 82.5 | 83.2 | -0.7 |
| Script 6 founder announcement / personal origin story | 82.9 | 82.8 | +0.1 |
| Script 7 personal technical walkthrough / agentic workflow case study | 84.1 | 82.0 | +2.1 |
| Script 8 product release announcement / personal observation | 82.7 | 82.6 | +0.1 |
| Script 9 career guide / hiring analysis | 84.0 | 84.2 | -0.2 |
| Script 10 engineering process walkthrough / presentation adaptation | 80.5 | 82.0 | -1.5 |
Each configuration name in the table above opens its own scorecard.
Pick a second model. A pair with its own written match-up opens that page, and any other pair opens in the comparison tool.
Every configuration wrote five drafts for each of the same 10 briefs, from the same prompt. Generation was closed-book, with the prompt as the only source and no web search or other tools. Each judge scores every draft once: Claude Opus 5.5 at medium effort through the Claude Code subscription, GPT-6.1 Sol at medium reasoning through the Codex CLI, GLM 5.3 Flash through OpenRouter, and Jev 1.13, a typed judge from TypeSafe that answers each metric as a multiple-choice question. The channel's finished script is their reference for the voice, and it gets no score on the board itself.
← All comparisons