Model Comparison
Too close to call: the best configurations, GLM-5.3 (max) and Kimi K3 (max), are 17 Elo apart, and their 95% ranges overlap. Among 78 models, each ranked by its best configuration, GLM-5.3 is #7 and Kimi K3 is #10. Cost per script: $0.240 for GLM-5.3 (max) and $0.362 for Kimi K3 (max).
Last updated , from board data generated at 01:13 UTC.
PickGLM-5.3
GLM-5.3 (max)
Pick GLM-5.3 (max) for most scripts, at $0.240 and about six minutes, a third less than Kimi's best with no gap the board can measure.
PickKimi K3
Kimi K3 (max)
Pick Kimi K3 (max) if it already runs in your pipeline, since its 2203 Elo sits inside GLM's range and switching buys nothing the board can measure.
Only 17 Elo separate GLM-5.3 (max) from Kimi K3 (max), and their 95% ranges overlap across most of their width, so the board can't split them on writing. Ranked by best setting, GLM-5.3 is the #7 model of 78 and Kimi K3 the #10 model. The metric bars agree: four go to each model and the slop score is level. That leaves the bill and the wait. GLM costs $0.240 a script and takes about six minutes. Kimi costs about half again as much, $0.362, and takes close to ten, even though GLM spends about two and a half times as many reasoning tokens. Kimi's cheaper setting doesn't change the picture. Kimi K3 (high) costs about half of GLM's price, then drops 242 Elo below GLM, with a range well clear of GLM's. Kimi gains 225 Elo from high to max, so with Kimi you're paying for the thinking, and stepping down gives up a lot. GLM's drafts are also a little steadier. With the writing this close, I'd start with GLM-5.3 and keep Kimi at max only if it already runs in your pipeline.
Disclosure: GLM-5.3 comes from the same lab as one of the four judges, GLM 5.3 Flash. The other three sit outside its lab, so it is never 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 Kimi K3, 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 |
| Kimi K3 (max) | 2203 | 2158 to 2243 | 82.2 | $0.362 | 9.9 min | 18,568 | #20 |
| Kimi K3 (high) | 1978 | 1936 to 2029 | 80.3 | $0.128 | 2.8 min | 3,081 | #37 |
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.
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 Kimi K3 (max) on four, with one tie. Weighted by each metric's share of the Writing Score, Tone & Voice Match does the most to put GLM-5.3 (max) ahead, with a 1.0-point lead on that metric. The weight beside each metric is its share of the Writing Score.
| GLM-5.3 (max) | Kimi K3 (max) | |
|---|---|---|
| Score spread across drafts (±) | 1.39 | 1.68 |
| Lowest script score | 80.5 Script 10 | 80.0 Script 10 |
| Highest script score | 84.1 Script 7 | 83.8 Script 9 |
| Range across scripts | 3.6 | 3.8 |
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 Kimi K3 (max), and the higher score is in bold.
| Script | GLM-5.3 (max) | Kimi K3 (max) | Gap |
|---|---|---|---|
| Script 1 opinion / warning explainer | 82.1 | 81.4 | +0.7 |
| Script 2 news-analysis / skeptical explainer | 82.5 | 82.6 | -0.1 |
| Script 3 personal roadmap / opinion | 81.5 | 81.8 | -0.3 |
| Script 4 short explainer | 81.0 | 80.1 | +0.9 |
| Script 5 news-analysis / opinion explainer | 82.5 | 83.4 | -0.9 |
| Script 6 founder announcement / personal origin story | 82.9 | 83.2 | -0.3 |
| Script 7 personal technical walkthrough / agentic workflow case study | 84.1 | 83.6 | +0.5 |
| Script 8 product release announcement / personal observation | 82.7 | 82.3 | +0.4 |
| Script 9 career guide / hiring analysis | 84.0 | 83.8 | +0.2 |
| Script 10 engineering process walkthrough / presentation adaptation | 80.5 | 80.0 | +0.5 |
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.
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