ResearchReport 02 · AI fiction · 22 October 2026

The AI Slop Index

We took the 1,321 scenes 13 AI models wrote for our last study and counted the phrases AI fiction is mocked for: the voice “barely above a whisper”, the “long shadows”, the air “thick with the scent”. Three models never used one.

0 vs 127
classic AI clichés, in about 25,000 to 28,000 words each: Claude Sonnet 5.5, GPT-6.1 Sol and Grok 4.7 used none. Llama 4 Maverick used 127, one every 200 words.
0.08
clichés per 1,000 words, frontier models
1.20
clichés per 1,000 words, open-weight models
3.82
clichés per 1,000 words, uncensored / writing-tuned models
9.7
em dashes per 1,000 words from Kimi K3 (Claude Opus 5.5: 0.04)

Readers can spot AI prose by its tics: the voice “barely above a whisper”, the shadows that are always long, the air “thick with the scent” of something. We wanted to know which models still write like that. So we counted, across more than 300,000 words of fiction that 13 current models wrote for our first study, using a public list of the phrases AI over-uses in stories compared with human writing.

The gap is large. Three of the frontier models we tested, Claude Sonnet 5.5, GPT-6.1 Sol and Grok 4.7, didn't use a single phrase from the list in about 27,000 words each. Llama 4 Maverick used one every 200 words, and the two uncensored, writing-tuned models were close behind.

Not every tic is gone. Qwen3.8 Max wrote the most “it wasn't X, it was Y” sentences, 3.2 per 1,000 words. Kimi K3 and Mistral Large 4 used about ten em dashes per 1,000 words, while Claude Opus 5.5 used one in 26,723 words. And names repeat: GPT-6.1 Sol called a character Mara in 23 of its 102 scenes.

What we found

  1. Frontier models have scrubbed the classic clichés. Claude Sonnet 5.5, GPT-6.1 Sol and Grok 4.7: zero phrases from the list. Claude Opus 5.5, Kimi K3 and Gemini 4 Argon: fewer than one per 4,000 words.
  2. Uncensored and older open models are where the slop lives. Llama 4 Maverick (5.0 per 1,000 words), Venice Uncensored (4.7) and Cydonia 24B (3.0) wrote 314 of the 381 listed phrases found in the whole study.
  3. The clichés that turned up most: “casting long shadows” (16 times), “air hung thick” (8), “words hung in the air” (7) and “took a deep breath” (7).
  4. “It wasn't X, it was Y” survives everywhere. Every model wrote some, from 0.6 to 3.2 per 1,000 words; Qwen3.8 Max the most.
  5. The em dash is a house style now, not an AI signature: about ten per 1,000 words from Kimi K3 and Mistral Large 4, almost none from Claude Opus 5.5 (0.04) and Llama 4 Maverick (0.08).
Chart 1 · The index

Clichés per 1,000 words

Phrases from a public list of the phrases AI over-uses in fiction, counted in each model's 102 scenes. Lower is better.

FrontierOpen-weightUncensored / writing-tuned
Claude Sonnet 5.5Frontier
0.00
GPT-6.1 SolFrontier
0.00
Grok 4.7Frontier
0.00
Claude Opus 5.5Frontier
0.15
Kimi K3Open-weight
0.18
Gemini 4 ArgonFrontier
0.23
Qwen3.8 MaxOpen-weight
0.29
Mistral Large 4Open-weight
0.43
DeepSeek V4 ProOpen-weight
0.57
DeepSeek V4.1 FlashOpen-weight
0.70
Cydonia 24BUncensored / writing-tuned
2.95
Venice UncensoredUncensored / writing-tuned
4.69
Llama 4 MaverickOpen-weight
5.02
Table 1 · Every tic, every model

The full table

Per 1,000 words, except names: the share of a model's scenes that use its favourite name.

ModelCliché phrasesOver-used words“Not X, but Y”Em dashesFavourite nameIts top clichés
Claude Sonnet 5.50.0015.390.890.18Mara (5%)none
GPT-6.1 Sol0.0015.880.640.87Mara (23%)none
Grok 4.70.0016.841.344.03Mara (5%)none
Claude Opus 5.50.1514.221.090.04Mara (6%)“first time in years” ×2
“pushed open the door” ×1
“first time in his life” ×1
Kimi K30.1813.401.359.68Mara (3%)“something else entirely” ×2
“one last time” ×1
“first time since” ×1
Gemini 4 Argon0.2335.040.755.02Kaelen (3%)“said, her voice dripping” ×2
“sighed, a sound like” ×1
“gaze locked onto” ×1
Qwen3.8 Max0.2912.343.216.55Mara (8%)“three days since” ×2
“like a living thing” ×2
“would one day” ×1
Mistral Large 40.4325.942.049.47Elias (11%)“three days since” ×2
“one last time” ×1
“like distant thunder” ×1
DeepSeek V4 Pro0.5727.621.586.34Mara (3%)“casting long shadows” ×2
“something else, something” ×1
“one last time” ×1
DeepSeek V4.1 Flash0.7019.542.144.12Elias (2%)“inside, the air was thick” ×2
“first time in my life” ×2
“brow furrowed in concentration” ×1
Cydonia 24B2.9536.081.944.82Elara (5%)“casting long shadows” ×7
“like a second skin” ×3
“air hung thick” ×3
Venice Uncensored4.6948.550.590.72Elara (3%)“air hung thick” ×5
“took a deep breath” ×4
“eyes wide with fear” ×3
Llama 4 Maverick5.0252.570.790.08Lena (8%)“words hung in the air” ×6
“air was heavy with the scent” ×5
“casting long shadows” ×5
The greatest hits

The clichés that turned up most

  1. “casting long shadows”×16
  2. “first time in my life”×9
  3. “one last time”×8
  4. “air hung thick”×8
  5. “words hung in the air”×7
  6. “took a deep breath”×7
  7. “air was thick with the scent”×6
  8. “eyes never leaving”×6
  9. “voice barely above a whisper”×6
  10. “eyes wide with fear”×6
Method

How we measured it, and its limits

The scenes. All 1,321 scenes the 13 models wrote for Banned by the Bot on 21 October 2026: 34 prompts (26 challenged books and 8 controls), three tries each, the same settings for every model. Refusals are left out. Each model has 99 to 102 scenes, about 24,000 to 30,000 words.
The list. Clichés are counted against the first 1,000 phrases (994 once case variants are merged) and the first 500 words of the public lists in Sam Paech's antislop-sampler (2025-04-07, Apache 2.0), which hold the words and phrases most over-represented in LLM-written stories compared with human writing. Each passage counts once, longest phrase first. The list was built from 2025 models, so newer models' own tics may not be on it: a low score means a model avoids the known clichés, not that it has none.
The other measures. Over-used words: the 500 words, per 1,000 words; many are ordinary words AI simply uses more than people do. “Not X, but Y”: sentences in narration that define a thing by what it isn't, found by the same detector KudoWrite runs on its own rewrites. Em dashes: the “—” character. Names: the share of a model's scenes that use a given name.
Limits. One prompt set, 34 scenes of about 250 words per model, three tries each. Clichés depend on the prompt as well as the model. Every count is in the CSV.
Who ran it. KudoWrite Research, with AI assistance for the analysis. KudoWrite's built-in AI runs on a DeepSeek “flash” model; DeepSeek V4.1 Flash scored 0.70 cliché phrases per 1,000 words here, 10th of 13.
For press

Use this data

The dataset

Free to cite and republish under CC BY 4.0, with a link to this page.

Download the dataset (CSV)
KudoWrite Research (2026). The AI Slop Index: how clichéd is the fiction AI models write? https://kudowrite.com/research/ai-slop-index

About KudoWrite

KudoWrite is a browser app for writing long fiction with AI. A chapter is one page of paragraphs: the writer types each one, the AI expands it, and a story bible and a running summary of the whole book keep up as they write, so chapter 30 is written with chapter 1 in context.

KudoWrite adds no content filter of its own: romance, horror, dark and explicit themes are the writer's call, within the law (a model's own behaviour still applies). No subscription: a free credit to start, then one-off credit packs from $3.50. Made by a solo founder in France. Contact: admin@kudowrite.com.

More from KudoWrite Research

Other reports

Your voice, not the model's

In KudoWrite you sketch each beat, the AI writes it, and you steer it with a one-line instruction or edit it by hand, so the clichés stop with you. The story bible and a running summary keep chapter 30 consistent with chapter 1.

Start writing free