GPT-5.4 nano
OpenAI · proprietary · writing benchmark
The short version
GPT-5.4 nano sits at #108 of 130 on ToneBench, toward the value end of the board, with a writing Elo of 902 and an overall score of 68.8 out of 100. We measured it by having it write all 9 of our real YouTube scripts, five times each, then scoring every draft blind against our own finished versions. Here is how it shook out.
Within OpenAI it ranks 31st of 36. GPT-5.6 Sol (ultra) is the family's top writer here, about 1414 Elo ahead, so this config trades some quality for whatever it saves you in cost or speed.
Among proprietary models, it comes in 80th of 83. That ranking is against the stronger half of the board: closed models still set the pace on voice fidelity here.
What it does best is Anti-Slop: it ranks 73rd on that dimension at 83.4, above the board average. Its softer spot is Length Discipline (18.3, 128th), which is the thing to watch if that metric matters most for your use.
It was uneven across the 9 scripts. Its best run was the news-analysis / skeptical explainer (72.9) and its weakest was the product release announcement / personal observation (64.8), a spread of about 8 points. Worth knowing if your writing skews toward one of those formats.
On cost, it runs about $0.007/task. Gemini 2.5 Flash-Lite scores higher for less money, at $0.002/task, so on pure value this config is not the frontier. Still, it beats 4 models that cost noticeably more.
Skill profile
The shape is the story: the further a corner reaches, the stronger that dimension. Biggest edge over the board: Anti-Slop (+0.6); furthest behind: Length Discipline (-48.9).
Per-metric scores
Nine writing dimensions, each scored 0–100 and blended by the editorial weight shown. Rank is against all 130 current-ranked models. The thick bar is this model; the thin lines above and below are the current field's best model (darker) and average (lighter) on the same scale. The small ± number is its run-to-run variation.
Per-article scores
The same 9 real scripts every current-ranked model writes, scored individually. Different formats stress different skills.
Consistency: run-to-run variability
Every cell on this page is the mean of 5 independent runs per script; the ± numbers are the run-to-run standard deviation. Overall, GPT-5.4 nano varies by ± 3.4 points between runs versus a board median of ± 3.8, so it is about as repeatable as the typical model on the board.
| Metric | This model | Board median | Verdict |
|---|---|---|---|
| Tone & Voice ? | 75.6 ± 2.6 | ± 2.2 | typical |
| Writing Craft ? | 73.0 ± 2.5 | ± 2.0 | typical |
| Substance & Value ? | 78.1 ± 2.2 | ± 2.6 | typical |
| Flow & Emotion ? | 64.3 ± 3.5 | ± 2.6 | typical |
| YouTube Structure ? | 66.7 ± 4.9 | ± 3.6 | typical |
| Hook ? | 76.7 ± 4.3 | ± 3.0 | typical |
| Length Discipline ? | 18.3 ± 6.2 | ± 8.6 | typical |
| Anti-Slop ? | 83.4 ± 2.6 | ± 2.6 | typical |
| Visual Cues ? | 73.1 ± 4.3 | ± 4.7 | typical |
Measured, not modeled
Numbers we log on every run and rarely talk about. None of these affect the writing scores; cost and latency are informational.
What each judge scored it
The published overall of 68.8 is the consensus of three family-disjoint judges scoring the same 45 stored drafts blind with the identical rubric. The highest and lowest judge differ by 15.7 points on its overall. That gap is wider than typical (board median 9.0): DeepSeek V4 Flash rates it noticeably higher than Claude Opus 5. Read the per-metric numbers knowing the consensus sits between two real opinions. How the panel works: methodology.
How we ran GPT-5.4 nano
- Accessed viaOpenAI API · model page ↗
- Exact model / route id
gpt-5.4-nano(provider default reasoning) - Audited route overrideOpenRouter ·
openai/gpt-5.4-nanoforautomated_video_workflow(OpenRouter gateway for exact legacy OpenAI model; Task-7 exact OpenRouter endpoint pin openai)
OpenRouter ·openai/gpt-5.4-nanoforai_engineer_hiring_2026, mentorship_release({'original_provider': 'openai', 'original_model': 'gpt-5.4-nano', 'provider': 'openrouter', 'model': 'openai/gpt-5.4-nano'}) - Generations5 independent runs × 9 scripts = 45 drafts, stored immutably after generation
- Run dates2026-07-29 → 2026-08-11
- Judges3-judge panel: Claude Opus 5, high effort (Anthropic) + DeepSeek V4 Flash (OpenRouter) + GPT-5.6 Sol, medium reasoning (Codex CLI): every draft scored independently by all three with the identical rubric, blind to model identity, evidence quotes required before each metric score; published score = per-metric consensus mean, verified with a Bradley-Terry refit (methodology)
- Samplingprovider-default sampling, no fine-tuning, no system-prompt changes; every current-ranked model gets the identical brief, style contract and research packet
- What stays privatethe exact rubric and any non-public reference material; source-publication timing is disclosed for exceptions (why)
Frequently asked questions
How good is GPT-5.4 nano at writing?
On ToneBench it ranks #108 of 130 with a writing Elo of 902 and an overall score of 68.8/100. That score comes from writing our 9 real YouTube scripts five times each and scoring every draft blind against our own finished versions across nine writing dimensions.
Is GPT-5.4 nano the best OpenAI model for writing?
Not quite. Within OpenAI it ranks 31st of 36; GPT-5.6 Sol (ultra) is the family's best writer here.
Is GPT-5.4 nano good value for the money?
It costs about $0.007/task. Gemini 2.5 Flash-Lite scores higher for less, so it is not the value pick.
What are GPT-5.4 nano's strengths and weaknesses?
Its strongest dimension is Anti-Slop (73rd on the board, 83.4). Its weakest is Length Discipline (128th on the board, 18.3). The full nine-metric breakdown is on this page.
Does GPT-5.4 nano write some formats better than others?
Yes. Its best of our 9 scripts was the news-analysis / skeptical explainer (72.9) and its weakest was the product release announcement / personal observation (64.8).
How consistent is GPT-5.4 nano between runs?
We run every script 5 times. GPT-5.4 nano's overall score varies by about ±3.4 points between runs, versus a board median of ±3.8. That is typical consistency for this board. The full per-metric variability table is on this page.
How was GPT-5.4 nano evaluated?
Via OpenAI API using the exact model/route id gpt-5.4-nano, run on 2026-07-29. 5 runs per script, provider-default sampling, no fine-tuning; every draft scored blind with a fixed rubric by a three-family judge panel. Full details in the 'How we ran it' section and the methodology.
Is GPT-5.4 nano open source?
No, it is a proprietary (closed-weights) model. Among proprietary models it ranks 80th of 83 for writing.