GPT-6 Luna ranks #33 of 87 models on the November 2026 ToneBench board through its best configuration, GPT-6 Luna (max). This setting, GPT-6 Luna (high), is #75 of 151 configurations, with a Writing Score of 77.4/100 and a Writing Elo of 1604. One script costs $0.0028 at API list price.
Last updated , from board data generated at 17:52 UTC.
Rank, scores, the nine metrics, the 10 reference scripts, cost and setup for one
configuration (a model at one reasoning setting). Key Results sums it up; the section links below it jump to the detail.
Model Rank ?
#33 of 87 modelsthrough GPT-6 Luna (max); this setting is #75 of 151 configurations
Writing Score ?
77.4 out of 100
Writing Elo ?
1604 95% range 1547 to 1647
Cost per Script
$0.0028 at API list price
Key Results
StandingGPT-6 Luna ranks #33 of 87 models through its best configuration, GPT-6 Luna (max). This setting is #75 of 151 configurations by Writing Elo (1604), in the middle third of the board. Its 95% range (1547 to 1647) overlaps 22 other configurations, ranked #68 to #90.
MetricsBest-ranked: Slop Score (85.7, 31st of 151 configurations, +4.5 against the board median). Lowest-ranked: YouTube Best Practices (69.2, 116th of 151 configurations, -7.2 against the board median).
ScriptsFrom 74.7 on Script 4 (short explainer) to 80.5 on Script 9 (career guide): a 5.8-point spread across the 10 reference scripts.
ConsistencyIts Writing Score moves ± 2.5 between drafts of the same script, against ± 2.5 for the median configuration: typical.
ValueNo configuration at least 15% cheaper ranks higher. It costs $0.0028 per script. The strongest option at least 15% cheaper is Gemini 2.5 Flash-Lite (no thinking), configuration #139, 1086 Elo behind, at $0.0023 per script, so nothing cheaper comes close.
Louis's PickI'd stay with this one: GPT-6 Luna (max) is the strongest GPT-6 Luna setting, but their Elo ranges overlap, and it costs under a cent more per script.
More Context Behind These Results
Writing Elo comes from comparing its average on each script with every other configuration's average on that same script; a gap smaller than the draft-to-draft variation counts as a draw (what the ranges can and cannot show).
On Slop Score, its best-ranked metric, its drafts carry fewer filler phrases and AI tells to clean out. YouTube Best Practices ranks in the bottom half (116th of 151 configurations), so expect to add the calls to action, open loops and re-engagement beats by hand.
The script format moves its score. If most of your scripts look like its best or weakest format, that result is a better guide than the average.
It outranks 64 configurations that cost at least 50% more.
Among proprietary models, GPT-6 Luna is 26th of 53. This setting is 63rd of 108 proprietary configurations. The best open-weight configuration, GLM-5.3 (max) at configuration #22, outranks it.
Lab
OpenAI 8th of 13 modelsthis setting 21st of 32 configurations
Weights
Proprietary 26th of 53 modelsthis setting 63rd of 108 configurations
Consistency
± 2.5 typical spread
Average Tokens
16.2k input + output
Time per Script
0.6 min average, retries included
Highest-Ranked in This LabGPT-6 Sol (max) ranks highest of the OpenAI configurations by Writing Elo, with a Writing Score +6.1 against this configuration, for about 12 cents more per script.
GPT-6 Luna is also on the board at other effort settings, and GPT-6 Luna (max) ranks higher, though their Elo ranges overlap. The thinking-level page for GPT-6 Luna charts how quality, cost and time change with the setting.
Each corner is one metric on a 0 to 100 scale; the further out, the higher the score. A corner outside the dashed line beats the board median. Hover or tap a point for its exact score, and a metric name for what it rewards.
GPT-6 Luna (high)Board medianBoard best per metricMax possible (100)
Scores by Metric
Nine metrics, each scored 0 to 100 and weighted into the Writing Score; the weight sits under each name. Rank is out of all 151 ranked configurations.
Score colors:Strong: 82 and upMiddle: 65 to 81.9Weak: under 65
This modelBoard best (upper line)Board median (lower line)±: spread between drafts?: what the metric rewards
Tone & Voice Match ?19% weight · -3.0 vs median
board best 82.8 · Claude Opus 5.5 (max effort)71.0± 2.0 · 99thboard median 74.0
Writing Craft & Clarity ?13% weight · +1.5 vs median
board best 83.2 · Claude Opus 5.5 (xhigh)76.5± 1.2 · 62ndboard median 75.0
Substance, Accuracy & Value ?15% weight · +0.8 vs median
board best 88.4 · Claude Opus 5.5 (xhigh)82.6± 1.5 · 65thboard median 81.8
Continuity & Emotion ?14% weight · -0.5 vs median
board best 85.7 · Claude Opus 5.5 (max effort)77.0± 1.5 · 86thboard median 77.5
YouTube Best Practices ?12% weight · -7.2 vs median
board best 84.5 · Claude Fable 5.2 (max effort)69.2± 5.6 · 116thboard median 76.4
Hook Strength ?10% weight · +1.2 vs median
board best 84.0 · Claude Fable 5.2 (xhigh)80.8± 2.2 · 55thboard median 79.6
Length Adherence ?8% weight · +4.3 vs median
board best 99.0 · Claude Fable 5.2 (xhigh)89.1± 6.4 · 54thboard median 84.8
Slop Score ?5% weight · +4.5 vs median
board best 89.0 · GPT-6 Sol (medium)85.7± 1.4 · 31stboard median 81.3
Visual Cue Quality ?4% weight · -0.4 vs median
board best 85.9 · Claude Opus 5.5 (max effort)74.4± 2.4 · 79thboard median 74.8
Scores by Reference Script
Every ranked configuration writes five drafts for each of the same 10 briefs, and each score is the mean of those drafts. The rank and board median compare it with every configuration on that script, which separates a weak result from a hard script. The result on the format closest to your own work is the most relevant.
Script 1
opinion / warning explainer
76.8
out of 100
80th of 151 configurations on this script · board median 77.2
Script 2
news-analysis / skeptical explainer
75.8
out of 100
97th of 151 configurations on this script · board median 78.4
Script 3
personal roadmap / opinion
77.2
out of 100
75th of 151 configurations on this script · board median 77.2
Script 4
short explainer
74.7
out of 100
81st of 151 configurations on this script · board median 75.7
Script 5
news-analysis / opinion explainer
76.6
out of 100
85th of 151 configurations on this script · board median 78.0
Script 6
founder announcement / personal origin story
79.4
out of 100
73rd of 151 configurations on this script · board median 79.2
Script 7
personal technical walkthrough / agentic workflow case study
78.8
out of 100
68th of 151 configurations on this script · board median 77.9
Script 8
product release announcement / personal observation
79.0
out of 100
76th of 151 configurations on this script · board median 79.0
Script 9
career guide / hiring analysis
80.5
out of 100
65th of 151 configurations on this script · board median 79.3
Script 10
engineering process walkthrough / presentation adaptation
75.0
out of 100
75th of 151 configurations on this script · board median 75.0
Consistency Between Drafts
Every score on this page is the mean of 5 drafts per script. The ± is the standard deviation between those drafts: the larger it is, the more two drafts of the same script can differ.
Compared with the typical model, it varies most on YouTube Best Practices: ± 5.6 between drafts against ± 2.4, so two drafts of the same script can score quite differently there.
Consistency by Metric
Metric
This model
Board median
Verdict
Tone & Voice Match ?
71.0 ± 2.0
± 1.9
typical
Writing Craft & Clarity ?
76.5 ± 1.2
± 1.5
typical
Substance, Accuracy & Value ?
82.6 ± 1.5
± 2.1
typical
Continuity & Emotion ?
77.0 ± 1.5
± 1.5
typical
YouTube Best Practices ?
69.2 ± 5.6
± 2.4
more variable than most
Hook Strength ?
80.8 ± 2.2
± 2.7
typical
Length Adherence ?
89.1 ± 6.4
± 6.6
typical
Slop Score ?
85.7 ± 1.4
± 1.6
typical
Visual Cue Quality ?
74.4 ± 2.4
± 2.9
typical
Cost, Time and Tokens per Script
Logged on every run; none of these figures affects the Writing Score. Time is the average time to one accepted script, retries included. Cost is one script at the API list price.
Time per Script
0.6 minfaster than the board median of 1.5 min
Prompt tokens in
13.2kstyle guide + brief + research packet
Tokens Out
3.0kscript + any reasoning tokens
Cost per Script
$0.0028provider-counted output, thinking included, plus the fixed prompt priced once; varies ± 0.001 between drafts
List Price Used
$0.1 / $0.5input / output, per million tokens
Scores by Judge
Four judges score the same 50 stored drafts blind against the same rubric: three LLM judges from three labs, plus Jev, a typed judge from TypeSafe that rates each metric on five described quality levels. No single judge's taste decides the result. The Writing Score of 77.4 combines their scores with the parts computed in code (Length Adherence and half of the Slop Score). The highest and lowest judge are 13.6 points apart on its Writing Score. That is close to the board median of 13.4. Each judge also has its own scale, so every card shows that judge's score against its average across the board. How the panel works.
Claude Opus 5.5 (medium)
Anthropic
72.2
+3.5 against its board average of 68.7
GLM 5.3 Flash
Z.ai
85.7
+2.8 against its board average of 82.9
GPT-6.1 Sol (medium)
OpenAI
78.1
+6.2 against its board average of 71.9
Jev 1.13
TypeSafe
73.6
-0.4 against its board average of 74.0
Evaluation Setup
How this exact configuration was run and judged: route, model ID, dates, sampling and prompt.
Accessed viaCodex CLI (OpenAI subscription) · model page ↗.
Exact model / route idgpt-6-luna at reasoning level: high.
Generations5 independent runs × 10 scripts = 50 drafts, stored after generation and never edited.
Run dates2026-10-11.
JudgesClaude 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 from TypeSafe. One call per judge per draft. All four score every draft on their own, blind to which model wrote it. The three LLM judges use the identical rubric and have to quote evidence before they score a metric. Jev answers the same rubric as one question per metric, choosing among five described quality levels, and writes no text. Seven metrics are the plain average of the four judges. Length is computed from the spoken word count, and Slop is half computed (lexical scans minus the spoken-dash penalty) and half judged. The Writing Elo is cross-checked with a Bradley-Terry fit of the same pairwise results (methodology).
Sampling and promptThe provider's default sampling, with no fine-tuning and no system-prompt changes. Every current-ranked model gets the same prompt, about 13,000 tokens (the count shifts a bit with each model's tokenizer): a style guide built from Louis's voice and storytelling guides, the target length, and the brief with its research packet. Generation is closed-book on every route: the prompt is the only source, with no web search, browsing, code execution, file access or other tool (generation).
What stays privateThe exact rubric and any non-public reference material. Each script is normally benchmarked before its video is published, which lowers the odds that a model has seen the reference, though it does not rule it out (why).
Frequently Asked Questions
How Good Is GPT-6 Luna (high) at Writing?
GPT-6 Luna ranks #33 of 87 models on ToneBench through its best configuration, GPT-6 Luna (max). This configuration is #75 of 151 configurations, in the middle third of the board, with a Writing Elo of 1604 and a Writing Score of 77.4/100. The ranks come from full YouTube scripts written in the voice of Louis-François Bouchard and judged blind, with the channel's finished script as the reference. The benchmark measures long-form scripted writing, so the result does not carry over to every kind of text.
Is GPT-6 Luna (high) the Best OpenAI Model for Writing?
No. GPT-6 Luna is 8th of 13 OpenAI models. The top one is GPT-6 Sol, through GPT-6 Sol (max), about 697 Elo ahead of this setting.
Is GPT-6 Luna (high) Good Value for the Money?
It costs about $0.0028 per script. That is good value: no configuration at least 15% cheaper ranks higher.
What Are the Strengths and Weaknesses of GPT-6 Luna (high)?
Its best-ranked metric is Slop Score (85.7, 31st of 151 configurations, +4.5 against the board median), and its lowest-ranked is YouTube Best Practices (69.2, 116th of 151, -7.2). The Scores by Metric section shows the board median and the board's best for each one.
How Was GPT-6 Luna (high) Evaluated?
It was run through the Codex CLI (OpenAI subscription), with the exact model/route ID gpt-6-luna at reasoning effort high, first run on 2026-10-11. It wrote each script 5 times with the provider's default sampling and no fine-tuning. The Evaluation Setup section and the methodology page have the details.
Does GPT-6 Luna (high) Have Open Weights?
No. It is a proprietary model: its weights are not public. Among proprietary configurations it is 63rd of 108 for writing.